{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 股票数据获取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 基础数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 获取股票代码\n",
    "    q = query(valuation.code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_fundamentals(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4724, 1)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000001.XSHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000002.XSHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>000004.XSHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000005.XSHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>000006.XSHE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          code\n",
       "0  000001.XSHE\n",
       "1  000002.XSHE\n",
       "2  000004.XSHE\n",
       "3  000005.XSHE\n",
       "4  000006.XSHE"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 要操作的股票：平安银行（g.为全局变量）\n",
    "security = '000001.XSHE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取股票的100天长期收盘价\n",
    "close_data = get_bars(security, count=100, unit='1d', fields=['close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(17.19,), (17.41,), (17.0,), (16.98,), (16.33,), (16.22,),\n",
       "       (16.52,), (16.5,), (17.33,), (17.35,), (17.2,), (16.85,), (16.65,),\n",
       "       (16.3,), (15.83,), (16.39,), (16.83,), (16.86,), (16.99,), (17.1,),\n",
       "       (16.58,), (16.28,), (16.41,), (16.42,), (16.77,), (16.51,),\n",
       "       (16.23,), (16.19,), (15.91,), (15.9,), (15.75,), (15.92,),\n",
       "       (15.68,), (15.71,), (15.33,), (14.72,), (14.31,), (13.84,),\n",
       "       (14.57,), (14.9,), (14.49,), (13.68,), (14.31,), (14.45,), (14.7,),\n",
       "       (14.59,), (15.18,), (15.0,), (15.2,), (14.98,), (14.85,), (14.68,),\n",
       "       (15.21,), (15.38,), (15.75,), (16.39,), (16.28,), (16.4,),\n",
       "       (16.05,), (15.92,), (15.8,), (16.04,), (16.42,), (15.9,), (15.81,),\n",
       "       (15.85,), (15.81,), (16.06,), (14.85,), (14.73,), (15.65,),\n",
       "       (15.65,), (15.32,), (15.32,), (14.96,), (14.55,), (14.59,),\n",
       "       (14.63,), (14.38,), (14.61,), (14.41,), (14.71,), (14.75,),\n",
       "       (14.62,), (15.02,), (14.83,), (14.4,), (14.39,), (14.19,),\n",
       "       (14.18,), (14.08,), (14.16,), (14.08,), (13.95,), (14.17,),\n",
       "       (14.16,), (14.16,), (14.35,), (14.32,), (13.91,)],\n",
       "      dtype=(numpy.record, [('close', '<f8')]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ==========================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>15.79</td>\n",
       "      <td>15.92</td>\n",
       "      <td>15.95</td>\n",
       "      <td>15.62</td>\n",
       "      <td>93504034.0</td>\n",
       "      <td>1.474694e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>15.79</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.87</td>\n",
       "      <td>15.66</td>\n",
       "      <td>76076086.0</td>\n",
       "      <td>1.196322e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03</th>\n",
       "      <td>15.73</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.81</td>\n",
       "      <td>15.63</td>\n",
       "      <td>57828505.0</td>\n",
       "      <td>9.081716e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04</th>\n",
       "      <td>15.62</td>\n",
       "      <td>15.33</td>\n",
       "      <td>15.63</td>\n",
       "      <td>15.28</td>\n",
       "      <td>99093566.0</td>\n",
       "      <td>1.523794e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-07</th>\n",
       "      <td>15.17</td>\n",
       "      <td>14.72</td>\n",
       "      <td>15.17</td>\n",
       "      <td>14.66</td>\n",
       "      <td>111315899.0</td>\n",
       "      <td>1.655960e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high    low       volume         money\n",
       "2022-03-01  15.79  15.92  15.95  15.62   93504034.0  1.474694e+09\n",
       "2022-03-02  15.79  15.68  15.87  15.66   76076086.0  1.196322e+09\n",
       "2022-03-03  15.73  15.71  15.81  15.63   57828505.0  9.081716e+08\n",
       "2022-03-04  15.62  15.33  15.63  15.28   99093566.0  1.523794e+09\n",
       "2022-03-07  15.17  14.72  15.17  14.66  111315899.0  1.655960e+09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 如果获取分钟数据， datatime.datetime(2019,1,1,10,0,0)或者'2019-01-01 10:00:00'\n",
    "# end_date如果只有日期，则日内时间等同于00:00:00，则不包括end_date这一天\n",
    "# frequency: daily(1天)， minute(1分钟)， Xd(x天), Xm(x分钟)\n",
    "# fields：要获取的字段，默认['open', 'close', 'high', 'low', 'volume', 'money']\n",
    "# skip_paused:是否跳过不交易日期（包括停牌、未上市或者退市后的日期），不跳过，\n",
    "#    停牌是会使用停牌前的数据填充，上市前或退市后数据都为nan\n",
    "# fq: pre: 前复权， None：不复权， post: 后复权\n",
    "df = get_price(security, start_date='2022-03-01',end_date='2022-06-10', \n",
    "               frequency='daily', skip_paused=True, fq='pre')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 数据操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>15.79</td>\n",
       "      <td>15.92</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>15.79</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03</th>\n",
       "      <td>15.73</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04</th>\n",
       "      <td>15.62</td>\n",
       "      <td>15.33</td>\n",
       "      <td>15.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-07</th>\n",
       "      <td>15.17</td>\n",
       "      <td>14.72</td>\n",
       "      <td>15.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high\n",
       "2022-03-01  15.79  15.92  15.95\n",
       "2022-03-02  15.79  15.68  15.87\n",
       "2022-03-03  15.73  15.71  15.81\n",
       "2022-03-04  15.62  15.33  15.63\n",
       "2022-03-07  15.17  14.72  15.17"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只显示 open, close, high\n",
    "df[['open', 'close', 'high']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-03-01    15.79\n",
       "2022-03-02    15.79\n",
       "2022-03-03    15.73\n",
       "2022-03-04    15.62\n",
       "2022-03-07    15.17\n",
       "Name: open, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选取open列数据, loc为location简写， loc[行标签，列标签]\n",
    "df.loc[:, 'open'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open      1.579000e+01\n",
       "close     1.592000e+01\n",
       "high      1.595000e+01\n",
       "low       1.562000e+01\n",
       "volume    9.350403e+07\n",
       "money     1.474694e+09\n",
       "Name: 2022-03-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取3月1号数据\n",
    "df.loc[\"2022-03-01\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022-03-01    15.92\n",
       "2022-03-02    15.68\n",
       "2022-03-03    15.71\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"2022-03-01\":\"2022-03-03\", \"close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>15.79</td>\n",
       "      <td>15.68</td>\n",
       "      <td>15.87</td>\n",
       "      <td>15.66</td>\n",
       "      <td>76076086.0</td>\n",
       "      <td>1.196322e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03</th>\n",
       "      <td>15.73</td>\n",
       "      <td>15.71</td>\n",
       "      <td>15.81</td>\n",
       "      <td>15.63</td>\n",
       "      <td>57828505.0</td>\n",
       "      <td>9.081716e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19</th>\n",
       "      <td>15.90</td>\n",
       "      <td>15.81</td>\n",
       "      <td>15.97</td>\n",
       "      <td>15.62</td>\n",
       "      <td>82177249.0</td>\n",
       "      <td>1.294227e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20</th>\n",
       "      <td>15.81</td>\n",
       "      <td>15.85</td>\n",
       "      <td>16.05</td>\n",
       "      <td>15.70</td>\n",
       "      <td>71655528.0</td>\n",
       "      <td>1.137698e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-21</th>\n",
       "      <td>15.77</td>\n",
       "      <td>15.81</td>\n",
       "      <td>15.98</td>\n",
       "      <td>15.70</td>\n",
       "      <td>70989116.0</td>\n",
       "      <td>1.124664e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>15.20</td>\n",
       "      <td>15.32</td>\n",
       "      <td>15.45</td>\n",
       "      <td>15.15</td>\n",
       "      <td>79019620.0</td>\n",
       "      <td>1.208735e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high    low      volume         money\n",
       "2022-03-02  15.79  15.68  15.87  15.66  76076086.0  1.196322e+09\n",
       "2022-03-03  15.73  15.71  15.81  15.63  57828505.0  9.081716e+08\n",
       "2022-04-19  15.90  15.81  15.97  15.62  82177249.0  1.294227e+09\n",
       "2022-04-20  15.81  15.85  16.05  15.70  71655528.0  1.137698e+09\n",
       "2022-04-21  15.77  15.81  15.98  15.70  70989116.0  1.124664e+09\n",
       "2022-05-05  15.20  15.32  15.45  15.15  79019620.0  1.208735e+09"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ===========================================\n",
    "# 条件选择\n",
    "# df[(df.one >= 1) & (df.one < 3)]\n",
    "df[(df['close'] > 15.0) & (df['money'] < 1.35e+9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 函数使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1018.3299999999999"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用std(), max(), min()计算标准偏差、最大和最小值\n",
    "# 计算收盘价之和\n",
    "df['close'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 均线值获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.83, 14.4, 14.39, 14.19, 14.18])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取倒数5天的收盘价\n",
    "close_data['close'][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取5天的均值\n",
    "MA5 = close_data['close'][-5:].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.398000000000001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MA5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 图形绘制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Dataframe直接绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f92d7ebef60>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "# 招商银行 600036.XSHG\n",
    "df = get_price('600036.XSHG',start_date='2022-03-1', end_date='2022-06-10', frequency='daily')\n",
    "df.close.plot(color='red')  # 绘制收盘价\n",
    "df.open.plot(color='blue')  # 绘制开盘价"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 matplotlib绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.24</td>\n",
       "      <td>50.50</td>\n",
       "      <td>49.36</td>\n",
       "      <td>40645436.0</td>\n",
       "      <td>2.027738e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-02</th>\n",
       "      <td>49.80</td>\n",
       "      <td>49.47</td>\n",
       "      <td>50.23</td>\n",
       "      <td>49.44</td>\n",
       "      <td>39707598.0</td>\n",
       "      <td>1.972076e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-03</th>\n",
       "      <td>49.58</td>\n",
       "      <td>49.73</td>\n",
       "      <td>49.97</td>\n",
       "      <td>49.45</td>\n",
       "      <td>31128967.0</td>\n",
       "      <td>1.546032e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-04</th>\n",
       "      <td>49.25</td>\n",
       "      <td>48.70</td>\n",
       "      <td>49.39</td>\n",
       "      <td>48.50</td>\n",
       "      <td>57789720.0</td>\n",
       "      <td>2.818734e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-07</th>\n",
       "      <td>48.01</td>\n",
       "      <td>46.76</td>\n",
       "      <td>48.26</td>\n",
       "      <td>46.50</td>\n",
       "      <td>101435369.0</td>\n",
       "      <td>4.773666e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open  close   high    low       volume         money\n",
       "2022-03-01  50.00  50.24  50.50  49.36   40645436.0  2.027738e+09\n",
       "2022-03-02  49.80  49.47  50.23  49.44   39707598.0  1.972076e+09\n",
       "2022-03-03  49.58  49.73  49.97  49.45   31128967.0  1.546032e+09\n",
       "2022-03-04  49.25  48.70  49.39  48.50   57789720.0  2.818734e+09\n",
       "2022-03-07  48.01  46.76  48.26  46.50  101435369.0  4.773666e+09"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "# 招商银行 600036.XSHG\n",
    "df = get_price('600036.XSHG',start_date='2022-03-1', end_date='2022-06-10', frequency='daily')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.1 subplot函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nrows 与 ncols 表示要划分几行几列的子区域（nrows*nclos表示子图数量），index 的初始值为1，用来选定具体的某个子区域。\n",
    "# plt.subplot(nrows, ncols, index)，可以不要逗号进行缩写"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close = df['close']   # 收盘价\n",
    "volume = df['volume']  # 成交量\n",
    "plt.figure(figsize=[12, 6])\n",
    "#激活第一个subplot\n",
    "plt.subplot(2, 1, 1)   # nrows, ncols, index\n",
    "plt.plot(close, \"-b\")  # 实线蓝色\n",
    "plt.title('收盘价走势')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(volume, \"-r\")  # 实现红色\n",
    "plt.title('成交量走势')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.2 add_axes()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#新建figure\n",
    "fig = plt.figure()\n",
    "myc1 = df['close']   #收盘价\n",
    "#新建区域ax1\n",
    "#figure的百分比,从figure 10%的位置开始绘制, 宽高是figure的80%\n",
    "left, bottom, width, height = 0.1, 0.1, 0.8, 0.8\n",
    "# 获得绘制的句柄\n",
    "ax1 = fig.add_axes([left, bottom, width, height])\n",
    "ax1.plot(myc1, 'c')\n",
    "ax1.set_title('新建区域')\n",
    "#新增区域ax2,嵌套在ax1内\n",
    "left, bottom, width, height = 0.2, 0.6, 0.25, 0.25\n",
    "# 获得绘制的句柄\n",
    "ax2 = fig.add_axes([left, bottom, width, height])\n",
    "ax2.plot(myc1, 'm')\n",
    "ax2.set_title('嵌套区域')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2.3 legend()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MBoPBYDBco1zKbuIKaLs7pZSqhn8+ky6LKlWqSP369f3Km5aWRnh4eNF2qJS2X5L7Vhztl7T+lKT2i7tvP//88xnRcU5LPUZ+XZn2S3Lfiqv9ktinktJ+cfYtLS2NgwcPli4Z5mPHz3H0NntHfMIRaHvB8+ht8q6HjSLeTdyqVSvxl02bNvmdtygoye2X5L4VR/slrT8lqf3i7huwU0rADrsrcRj5dWXaL8l9K672S2KfSkr7xdm3TZs2lToZVthwdIIOtP4q2k9RQ7Q7AYPBYDAYDAZDKeRSlokT0I4+bSLym1Iq5wr3yWAwGAwGg8FwlSjszKDBYDAYDAaD4RrC18xgZbQ3eyt6FtCBQntJj0CH3jEYDAaDwWAwlGJ8KYORPq6/jY5GAvCt/W/+EaoNBn85+DUEBkOj+4q7JwaDwVAofjubxn92/8GYexuhlHktGkoXXpVBEZnleq6UGo7eMfx3L3nrK6VsRdQ/v0hOTiYhIYHs7GzKly9PTExMsfWlJLdfkvsGQHIZsARCEfXRs/3ieB5BQUFUq1aNcuXKXdV2DYaSjEOGl1b5ZRMhMSWTZqHC/gMxBFiuvDLorf2S/EyKu/2i6tu1KsP92kAiIh8VkKX7FejLJZGcnMzp06epVasWoaGhpKamUrZs2eLqDikpKSW2/ZLcN7IvQmIGBIVB1RuvSvtX+3mICBcvXiQ+3mzANxgcuMrwnJwcny/Zkiq/RIQT5y9iLZcFQOMaZQkOvPLud721X1KfSUlovyj65inDryWF8EptINl/heopNAkJCdSqVYuwsDAzNV+ayUjWf73Eyr5WUEoRFhZGrVq1SEhIKO7uGAwlgtIuw5PSszmfnkVokFYAr2ERdt1zLctwn8qgUqqWUupXl/MqSqmRXvKFAjFKqb8UUR/zJTs7m9DQ0IIzGko2mQ5lsFgtDq4KoaGhZGdnF3c3DIYSQWmX4RcuZhMcGEC1ssEAjsANhmuYa1GG5zczGAjUA1BKhQCrgfFKKc950S7oHce/FEkP/aA0jiYNLtiskJVmP7n2Ban5vpYclFIhSql5SilRStUvIO/flFLxSqkMpdR2pdQdV6eX1z6l+TdhtQmBAQpltxO0XQERlpWVxYkTJ/zKe/LkyWIxPUlJSWHZsmVkZma6XU9ISOCbb765qn3Jybm67o5L8/fVF/4uE78J1AW6i0iyR9ojwA8i4t8311DklBZB8uWXX2pBkpUKCAQEk5CYeMmC5NChQ1y4cKFQZU6fPl3odqKiokhJSXGeW61WvvzyS9LT0wtdl6H4UEo1A3YAdfzIOwL4G/AM8CdgL7BOKVWlSDtpKPHYRAhQCgtwcH805cNDnGkhISEopbweffv29VnnK6+8Qvfu3bHZCl4piYyMZOzYsc7zDRs2+Gxz3759ecq/9957vPnmm4COqXvixAmvx7lz59zKbdu2jeeff57AQPetB3PmzOGll14qsN/eqFGjBitXrmThwoV0794dq9VaYJlu3boxdepUv+p/7bXXCA0NpXbt2j6PMmXKsHjx4kvqf2mmwA0kSqm7gGFAJ+A+pdQ7wNMickIpdQPQH3iiSHt5nbB7925at27tHOWEhITkGXU5ePTRR/nqq6+8pr3yyiusXbuWPXv2YLHkr+9HRkaSmJjIsmXLAC1IunTp4jVvdHQ0zZs3d7v23nvvkZGRwXPPPUdaWhrnz5/3WjYsLIygoCDn+bZt2xg9ejR9+vSBlGRQFgguy5yFs1m/9Re6du3qtZ5p06axb98+li5dyokTJ6hTp45zWWb8+PG0bNmSmTNn5nvPDjIzM2nRogX//Oc/GTx4sF9l/vvf/zJkyBB+//13p3Hy66+/ztSpU/njjz8ICzPuN0sR7YAFwCrgWAF5RwELROQ/AEqp0UAfYADwblF20lCysYlgsStbnhw9ehQRYeLEiQQGBjpl05QpU5yyvXbt2j4H5AEBeTeitG/fnq1btwJ6881nn33G9OnTSU1NdfahatWq7Nq1y61cnTp5xzx79+5l4sSJREVFAbBo0SLGjBnjtS+9e/dm6dKlAMybN4+lS5dStmxZp+I3Y8YMRIQFCxYwceJEr3UURIUKFcjIyGDAgAHs3r2b+Ph46tat60z//fff3RTk1NRU2rZty7vvvsvQoUPd3jGpqalUqFCB2rVru7XRoEED/vrXv/rswzvvvHNJfS/t+DMz+DPQT0S2AeuBssAupdRtwPPo2MRfFl0Xr1+OHj3K8ePHeeyxxxg0aBDHjx/n+PHjDB061Dkaq127ttvIr1y5csyaNYt9+/YREBCQJ+2uu+5y1u8QJB06dCA1NZW0NL1UW7VqVWdbjsMbDkHSrFkzQAuSOnXqeD1GjBjhLDdv3jxef/11ypUrx0svvcTkKZHkWELIttpY8PkKBgwYULgHlXYGycnip59+crs/X9x5550opahatSoJCQkMGTLErxE0wOzZs3niiSeoUkVPCK1fv55XX32VO++8kz59+jifoaHkIyILRaRARU4pFQw0R8tCR9ksYDfQpuh6aCgNWG0Qf+I3IkKC6NetI1arFaUUQ4cOpVatWtSuXZs9e/bQtm1b5+xTQEAAISF6BnH79u0cO3aMmJgYqlSpwj//+U+OHTvmPHr16kXz5s3Zu3cvx44dcypkoGe6UlNTGT9+PGXLlqV8+fIAWCyWPDNenhw+fJhu3brxxBNP8MADDwAwevRoRMTr4drukiVLCAsLo0uXLqSkpDBr1ixycnL44IMPOHXqFBMmTHCTp40bN3aWPXHihM+Zy0OHDvHYY48RFhbG3LlzqVevHvPmzXOW/fOf/0yDBg2cx80338y0adM4d+4cTZo0yZPm7X0QGBhIRESEz6OgCZRrlfxmBoMAROQisNL++XelVCdgIfAdEACMELkOrP6LkLi4OBo0aOA8V0oxZMgQ51T1nj17GD16tPMH7SlIcnJyyMjIoEOHDowbN46BAwc665owYQKxsbGsWrWKtLQ0pxIDelbQIUjGjx9PQEAA69evdwqS/PAlSEaPHu2zjGNpdcmSJVSqVInu3buTknyB9z9YyLRXpjB/4b84lXCGCRMmMGHCBGe5Ro0aERsb67szF46z+9g5zpw5w4MPPpgnOTY2lkaNGrldmzt3Lo888gjr16/nyJEjvPjii840X+4IfvnlF1asWMGhQ4cA2LRpE4899hhLliyha9eudO/enQceeIBVq1Y5hbLhmqAyeuB8zuP6OaCatwL2ZeURANWrV2fz5s1+NZSamup33qKgONovX768UzZYrVY3EwxX8ku7Gvhq3yo2qlerzt59B/hxdwyjh/Rl3759hIWFkZKSwvr164mNjaVz587O8ufOnaN69eqkpKS4yYrJkyezYMEChg8fjsViISYmhjVr1vD1119Tp04d50xhSkoKMTExzJs3jzlz5jBkyBBnHZs2beLcuXPcfvvtefqalpZGSkoKGRkZdOzYkfbt2zNjxgzWrVtHu3bt/FaErFYrQ4cO5d5770VEeP/999mxYweTJk3i73//O48++qgz74oVK/j444+d956amgroWT5PhgwZQrt27Xjqqaec1xzPEbRZk4OVK1dy5513UqNGDbf72717N+3bt8dqtRIQEOD2P8vMzCQmJobJkyf7vLfU1FQyMjIK/K5lZGT4/K047rE0kUcZVEoFANOAPDuHAURE7E6o70ULyRVF2cHrgTp16nD8+HEOHDjAAw88QFxcHOHhOgrg2rVriY2N5eGHH3bmT05OpmbNmgBuStu0adOYO3cuL774IhaLhf3797NmzRq2bNlCgwYN3Pwu7d+/n3fffZePPvqIYcOGOevYsGED586d48477/TZ34yMDO6++246duzIO++8w5YtW+jQoUOhRlTPPPMM3bp1Iycjjfc/mMf+Q0eYNHUms14ay4ARE8Cihd5XX33Fhx9+WGB9a9d/Q8eOHfnss88AOHv2LC1btmTr1q1uijbA+fPnqVGjBhEREfTo0YNJkyaRnJzMDTfc4LN+q9XKiBEjuPnmm2nYsCFffPEFTz75JO+++y4PPfQQAKtXr+b+++/ntttuIyoqittuu83v52Eolfi0IheR+cB8gNatW0unTp38qnDz5s34m7coKI72Y2JinHKptPlJFRHkwgXCw8OoVaUJMSfOANC0aVNAr56MHDmSYcOGuc2OHTt2jLZt2zrr81xirlChgtu5w3SnVq1aTnvwFi1aMH/+fCIjIxk3bhwAPXv2ZOTIkVSsWJF169a51VG1alXCw8MpW7YsZcuWJSoqivbt27Nx40Z69+5NTEyM12VpzzqCg4MJCAggLCyMgIAA505wx3fnpZdecnsXVK9enaCgIOe9RkREAN6XrWvXro3NZqNOnTqcOXOGkJAQZ35XIiMjmTFjBp988onbStKGDRvo168fw4YNY9q0aVSrljtWmz9/PtWrV2fOnDn53qODr776iieffNJnekhICLfeeqvXtOIc0F0qeZRBEbEqpVqj7WB8WWVOB0KARGA08EaR9fA6ICAggNq1a3PmjBYkDgVv7969DBkyhGHDhjmVP9AzXa6jPk9B4vmDbteuHQA33HCD0zblT3/6E59++imTJ0/m6aefBtwFyZo1a9zqqFq1qvNzSEgIX331lVOQ9OzZ0y9BEhISkleYizYQXvt/G+nUoR3PPz0YS81aEKC/mhUqVCiwXoDPo1Zwb+euzmf322+/ERgYyB133OEs37VrV3766SesVit9+/Z1e27/+te/3Opr2bIld911l/NHPX36dHbu3EmrVq145ZVXmDt3LkuXLnXOioIewa5bt47x48fTrl07fv75Z2666aYC+24o8ZwDbEAlj+sVgT+ufncMJQWb3V5Z2wzmXj916hTR0dH079+fli1b8vbbbxMVFUVkZKRzxq9z585ude3YsYNmzZrx+eefc++991KjRg22bdtGdnY2HTt2ZPXq1bzwwgvO/CEhIfTp04cnnniC2rVrs2HDBpo2bcqGDRvIyMjII8M96dChA8ePH+fxxx9n6tSp1KpVy83mzhubNm3yOViYOHEiSil27NhBSkqKU4G1Wq15Npn4olatWs4ZwwULFrB8+XJ27NjhTN+zZw9jx45l+/btfP7553Tr1s2tfK9evdi8eTN9+vThp59+Yu3atU6FMCkpya8NOQ7KlCnjd95rAV/h6LorpeoBU+275dq7GE6/DIxFzwxWBxYppT4UkeKbv/cgcvV+Dvzhuen56tC4Sigzere8rDouR5B8/PHHPPDAA3kEyYYNGxg1apQzf0hICP3796d///5XTZCsXbvWTXkCwP7jnPj8eJQ1ix0/biBl32m6dNP5/BEkW7b9TMyhWE4nnuWf//wnoaGh/O9//6N58+ZuZb/55hs2bNjAuHHj+O2330hJSWHfvn00b97cTUlVSrF7927nZplt27bxj3/8g0cffZRff/2Vdu3aMXToUBYvXszGjRudO/G+//57+vTpw5kzZ+jVq5dRBK8RRCRDKbUPaA18CqCUCgJaYjehMVxZvMlwx7JfUfPnG8ox9S/+/XYduoXFouXGH8d/x2q1Ur9+ff72t7/xzTffcOutt2KxWOjcuTMZGRkkJSXRrl27PLNKoaGhzJs3j9dee40dO3YQERFBUlISI0aMYNiwYdxxh/+ejLKysti8eTPr16+nSZMmNGzYkCFDhlCxYkVnnvj4eO677z6qVavmVOQ8fSQeOXKExo0bk5iY6GZiBNC9e97AY8HBwWzbto3XX3+d2NhYIiIiyM7OJjg42JnH0YbrYDw8PJzU1FQaN27Mxo0bAW2P3bFjR7f6p06dilKKXbt2cfjw4Tzt//bbb0RHR7Nlyxbuv/9+oqKinO+9iRMnEhkZydtvv13g85s8ebKbqdL1gD/rencDXymlHlFKPQxMAnqJyA4RWQ2cQLtbMFwmcXFxTkGybds2vvnmG7799luCg4Pp3LkzkyZNYvjw4Wzfvt2nIHEojA5BMnDgQCIjIws1ynEIksmTJ/Pxxx+zefPmAgVJYGBgHoNjh51fYmIiIkKHDh2c5bt3745SiqAIe53KQnBICNt+3svgJ5502lx4ChJv/HbiJM88OYiaNWs67SzXr1/vdQT74Ycf0qP6xsb0AAAgAElEQVRHD+f5q6++SocOHUhO1i8ex+ysqxLZtGlT5s6d61Rku3XrRsOGDUlOTvbpysa1DUPpQym1XCnluh3zPeBJpdRDdi8Kc9DLxJ8XSwcNJQKrXbHZt3s3LZrfxPjhj2OxWPjxxx955ZVXeO6555wb+SpVqsTgwYMZO3YsrVu3zuO+ZO3atbzzzjt8/fXXNGzYENBeI3bs2MGWLVucssmVpk2bopQiPj6eZs2aoZQiIyOD8uXLs3jxYh599FHatGnD4sWL6dmzp3OWLSEhgTZt2mCz2ahcufIl+c379NNPiYmJIS4uzu36qFGjsFgsfPLJJ4B+n7h6WUhPT3cqniLCunXrnO+nW265hejoaI4dO8bWrVvp2bOnW91LlixxrtYMGjSIWbNmOdNsNhuDBw/mP//5D40bN2bLli1uEyAAFy9epEWLFixYsIAFCxbw6quvcuHCBd544w3ntWbNmpGRkVHo51HaKXDuVkSWKaVmAp8BtwF3iMgBlywfAcOBWd7KFwf+juqKgksxcN61axcDBw7k4MGDTkHSqlUrOnXqxJYtW7yWWbRoEUOHDnWer1271rl06SpIWrZsycCBA73OUrm6NHDsCF69erVTkIwZM4bg4GDeeOMNVqxYwY4dO+jVq5dTkISGhl6WIOnUqRPW1LPUb9ZS2wgqYdTQfrzx0Rd88sknjBo1Ko8g8cbgR3vw+MByLP3uZ0aNGsVdd93Fpk2bmDZtmlu+uLg4vvvuO9566y3ntX//+998+OGHlC1blrfeeospU6ZQrlw5N3cGFStW5KmnnsojvNPT06+7pYTriEBc5KOIzLevknyAtpXeA3QTkbxvaMNl402GF7fNoDccy8T16tXjscceo3ytRjz31GBatWrlzBMZGZnHdZXDztiVBx98kOHDh1O5cmXKlSuHUgqr1Up4eDinTp1i+fLlecrs2rWL8PBwbrzxRlavXk2TJk3Yvn27M/2+++7jmWeeoWnTpowePdo5K1atWjUWL17M6dOnWbBgwSXde5UqVahVq1ae6DFlypRh5MiRTr+raWlpbv+3hIQEt5i+Fy5ccNoF3nLLLQQEBDBixAiaNm1K+/bt3ep25LvppptYvnw5Dz/8MDk5OcyYMYMRI0awf/9+fv5Zb/r3tLt0kJaW5lRgHa7Qjh8/TlJSkjP9esSvhXwRiVRK3QvMFZH7PJKXAu8opRqKyK9eihsKoF69egwYMIAWLVrQu3fvIhEk69evz1Pm8OHD2Gy2YhMktWvXJifJvuyjAkDZKFMmiJEjhvsUJKBHmt98842bqwOLgr59+zJv3jw6depE8+bNnbaSDurXr8/333/PDTfcQFJSkvPl8sQTT3DhwgW6detGUFAQrVu39svZ6cmTJ30aEBtKDyISh8dmEBHJ8yMTkZmAf04sDdcFNnu4kSpVqzBlyhSivtmaJ0+lSpWoX7++2zVfg0iHknTo0CFq1KjBzp07811lCAsLIyIiwhkz13OzRY8ePZgwYQJjx45l4cKFbp4munTpwpIlS/y6z8Ly8ssvOz8nJSVRuXJl5/nu3budExagFTLHEnRgYCDdunVj2bJlfPnll/luSuzatSurV69mwIABfPHFF5w/f57vvvuOevXq5du36Ohojh49CuC0IZw9e7ZzUuN6DR6Q3zKxDXB9KmOAZKWU2zSNiJwG9gPui/sGv6lcuTJTpkzx+iV2CBLXwx9BkpSUxKZNm/Jt1x9BEhIS4hQkI0fmbjD35Zi60Ng3kOjdw/rH+PLkF5yG0p6CJDw8nNWrVzNmzBj3EER270a9e/fm3LlzPvs3dOhQKlasSMWKFalbt67zc8WKFbnpppsYO3Ys7dq1o23btvl3W4SdO3e67RA0GAzXF1a7iV2A0jZwxRmkLCcnJ89KksVi4fnnnyczM5OzZ89e0fbOnDlDfHx8gRGsTpw44eb1YtWqVbRr14709HR++ukn9uzZ43T9lZCQwLFjx1BK0aaNduG5du1an4Pzs2fPEhYWxpkzZ7BarXz22Wf59ue1114jKyuLpKQkkpKS2Lt3L5D7zkxKSiIrKyvPqtL1gE9lUESOi0hZl/O9ItJLRLypzd1FZHFRdNBwdSgOQXLixAniT9h/uMqiD3AqdpBXkDz77LPExcVx6NAh/v73v+dWKDa+/vprJkyYwEMPPcTbb7/t05N8VFQUIsKAAQOYOnUqIsJLL73EU089hYjwxRdfFNj/b7/9loSEBJ+RUgwGw7WPY5kYEVavXs3BfXvybHgbM2ZMHufKjqVMT5RSlC9fnqZNm1KhQgU6d+5M+fLlOXnyJLGxsfluprvnnnuYMWMGoG3jxo8fT7169Th69CgzZ87k2WefpWfPnl5tDz2JjY3lv//9r7Of3todNGgQzZo1yzPrCXrpNy4uju3bt/Ptt986vV8cOXKE7777jv79+5OVlUXXrl35/PPPefrpp1m+fDmtWrXCYrFw77330q9fPz799FMGDhzoNvA/deoU7733HrfccgtPPfUUTz/9NImJicyfP5+VK1c6N8usXbs2zyyf58SKwyn17bffnifNn9Whawn/9nv7QOl51UARMe4VLgObzcbatWtJTEz0Kkh8hQfyxFWQOKhevTonT57k0KFDBQqSli1bMmnSJKcgWbp0KX379nUKko0bN7Jw4cI8u8o8iY2NJTEx0Rm5xJcg8XIDAFy4kMT5U+c4deoU3377rdtSdJkyZXJnUO2COCMjk1dmzuXNDxYzbtw43nzzTRYtWsSTTz7J//73Pz788EOCgoL47bffyM7OJjExkbi4ONLS0khKSiIuLo4LFy6QkpJCXFwciYmJZGdnExcXR82aNfNsYDl9+jSjRo1i+PDhVK9ePd9nYbj6KKU6isj3xd0Pw7WPY5k4MDCA6dOnszc6mnu63O+WZ/r06W723YBX5/ig3YI5bNdcadWqFTExMT53uD733HPUqVOHRx55hNjYWCpVqkRiYiJz5syhR48eBAYG0qRJE95//32nD9v82L9/Py+88ALJyck89NBDeezvGjZsyKxZs2jRogURERFOGevg3Xff5Y033iAzM5OePXs6l7qtVitjxoxxKmGu4UvHjRvHgAEDmDZtGtnZ2QwdOpRRo0Yxe/ZsgoODiY+Pp3v37k4PEP3792fw4MHOyYKBAwfSr18/vvzyS+bNm+eMZBITE8OBAwe87n524PDd6EpgYGC+rnSuOXyFnrFv/x6BVvZ8pT+A9sE1JL96Lvdo1aqV+OLAgQNu58nJyT7zXg0utf3bb79dQkJCpFevXs5rd999t0yfPl2OHz/udrRo0UIWLVrkzAdIdHS0z/Zvu+02CQ0Nleeff95r22+99ZZERUWJ1WqVgwcPSv369WXgwIGyYsUKyc7OFhGR5cuXS+fOnSU9Pd1Z7tNPP5W77747T30rVqyQRo0aSbVq1eShhx5yXnf0bdCgQbJ161YREbGdOSofzHpFcnJyRLLSReJ3yd+nvCjlypWT4OBg6du3r7MPnhyP+1UAifvfGmnWuKFERUW5pa9cuVLuuOMOSU5Olk2bNglQ6OOHH35w1rdo0SJp1aqVrFmzRu699948z3rTpk1SuXJlr3315MCBA7Jp0ya/8hYV+bVf3H0DdopvufNXYJyPNAtg9VW2OI785Jcnxf3ci6N9Vxmen/wsibI9Ifmi7Dl+XnKsNhER2f/HBTl+Lq1Q9Z5MSverjLf2S+IzKUoWLlwohw8f9qv9/fv3y8qVK4usL566hyv2941PGVYSDyXi7lcInDN+tYE4tGNVK7AR+B5YKSL/dck7CJgjIt637lwBWrduLTt37vSaFhMT49wJC8W/46wkt18i+3b2CNisUPVGyMmEhANQoR6Eefr39UJWOpw5BBbt1kbVbJEni4g4DYM927+c5+Far4OcnBzS09Pddsr5IiYmhtOnT5fYaBPFHQlDKfWziLS2fx7skRwKVEAr7CH2ayIi05VSx4C6IlL0Dun8JD/55UlxP/fiikDikOGlTX6dupBBQkoGN9cqj1KKgyeTCQ8OpE6l/D0guHIkIRWbCE2q539v3tovic+kpLRf1H3z1D1c2bx5M/fcc49ThpUGfK0brgGOupyHoJ2r3g+MV0rFAJHAT8AotKJoMBQem1XvJAbnMjH+hrq2Zeu/AcGonItes1yK2xt/8FZvYGCgX4qgodB8BJxCK383oKMeAUwGYoAI4EZ0ZKTitOE3XGfYRAiw2wGClgs2LxMs+ZFjtZlvraHY8bWBZBkwEftXVETOishrInIP0BgtmL8CfgWSgSE+6jEY8keszjjEzq+jv8qg1a4MBgbrMoUUwoZSQwLwDvAq4OkE7E3gX+R6PjBfAsNVwyaCxZKrySlVODEkImTbxIguQ7Hja2bwDODwszVGKRUK1AWaADej3c4sBxoA5YHsIu6n4VrFZstVBp0zg35KRpt9h1lgmdxyRTQTaCgxmNemocRgswkWF5ljUYX7glpt2l7LZr7VhmLGlzL4IZCE/l7fDZxEh53bhPa8v0dErEqpasBOYCrwt6LvruGaQ1yXiR0T1YWYGVQBoOxfY7HhX4RFwzVEBaAs2tS5GlBibAUN/uPNBrc0YBWtADpQ5I3vmx85di3QKIOli8L8j0sLXpVBEakJoJSyAX1EJNlHvgSl1LMYh9OGS0Fs+nCbGSzEOostGwKCXPwTWrlMb0mG0oVC2xM6OGm/du1J6muYoKAgLl68WGDYyZKIzZZ3mbgwil22VQ98nTs6S6FCfD1y8eJFN1c61wKFmkZRSiUrpc4opeKUUjuVUv8GzovI+CLqn+Faxh4KKHdG0P65MDaDliCwFNLW0HCtIOgNbK8CKUBDIP9wCIYSR7Vq1YiPjyc9Pb3Uzbg4NpA4UKqQM4PW3LxmdrDkIyKkp6cTHx9PtWrVirs7V5TCTqNEAN3RdoIVgVuAFfbPBaKUuh+9MeUOIAO9C3mciJwsZD8M+ZCVlUVCQoJb5A5fnDx5EpvNRq1ata5Cz3JJSUnh69X/4eG2jQm25K7sJZw9x+5t0XR9ZGA+pe3YcqBMiNfIJYUhJycnX4fchhJNAhAO2ETkN6VUTkEFDCULxw78P/74g5SUFEJCQrzmy8jI8Jl2NfDW/qkLGZQJtHAxUdstn03NJMcm5Jzzr58pGdlcuKi/spYLIQRYfM8Memu/JD6TktJ+UfUtKCiI6tWrX3OeIwr7BhQR+T/HiVLqdqC/PwWVUoHAs8B/gKFoAf5v4BPAxPRCB/Bu3bq1M/ROSEgImZmZXvM++uijfPXVV17TXnnlFdauXcuePXvyDfQNEBkZSWJiIsuWLQNgw4YNPuP6RkdH07x5c7dr7733HhkZGTz33HOkpaW5eZR3JSwszG1afdu2bYx+djx9dq3LtRkE5nz8b9Zv3lawMiiiZwYDAp3KYI26DZk370POnj3L0qVLWbNmDQEB+ZuQdevWjVatWvGPf/wj//bQcS0jIyPdYiV7kpCQwPz58/NEHDAYDL4pV64c5cqVY/Pmzdx6661e8+SXdjXw1v6gGRvoelN1ZvbS/ubGfPEL++IvsOl5//o5fc0BFm7VkZq2TrqH2hV9L5V7a78kPpOS0n5x9620UZAy6IjE4Ito9I7iAhGRHHTEEidKqY+At5RSSkrb+sBV4OjRo4gIEydOJDAwkJkzZwIwZcoUp5JYu3Ztn4G5vSlC7du3Z+vWrQAkJyfz2WefMX36dFJTU532KlWrVmXXrl1u5erUqZOnrr179zJx4kSioqIAWLRokc/Qeb1792bRokUAzJs3j6VLl1KubAQvzXoPQisy49XXEREWfLaMiWOfKvDZYLMCopeJ7cpghfLlycjIYMCAAezevZv4+Hjq1q3rLHL8+HE3u6TU1FTatm3Lu+++y9ChQ/PYgAQGBuaZXW3QoAF//etffXbLVzxkw2VRGR11xIoeRDpQwFj0ikXpMzgzlHrSMnMIL5MrZ8sEWMjK8X+FIiEld7CfkX19xcI1lCwKUgYVkORi1KrsyzDJaB+D/wW+ALZdYvs3AH9c74pgXFwcDRrk6tRKKYYMGcLixYsB2LNnD6NHj3YqJgEBAc7p7+3bt5OTk0NGRgYdOnRg3LhxDByYO6s2YcIEYmNjWbVqFWlpaW5xhSMjI0lNTWX8+PGMHz+egIAA1q9fj8ViKXCJ+fDhw3Tr1o0nnniCBx7QOv7o0aMZPXq0zzIpKSkALFmyhEqVKtG9a2dSUi/w/nuLmTZ9JvPnz+dUwhkmvPwPJrycO1PXqFEjYmNjAR1D0pti6uCxxx5zfp47dy4ffPABTz/9NABt2rQhLc3TTZ2mSZMmea7Vq1ePuLg4t2uBgYFERET4bL+gmVjDJRHp4/rb6GgkAN/a/xoLfMNVwWoTLmZbCQ/OfY2WCbSQWRhlMDnD+Tkj29g8G4qPgpRBz7euQo/AqwEt0b4If1BKvSsi3iNo+0ApVRV4EnjdR/oIdGxkqlevzubNm73WU758eaeSAToQtuv51eZS2q9QoQIxMTEcPHiQPn36sG/fPsLCwkhJSWH9+vXExsbSuXNnZ73nzp2jevXqpKSkUL58eWc9kydPZsGCBYwePRqLxUJMTAxr1qzh66+/pkqVKlSsWJGAgABSUlKIiYnh3XffZc6cOQwZkuszfNOmTZw7d47bb789Tz/T0tJISUkhIyODjh070r59e2bMmMG6deto165dgYqQ49lYrVaGDh1K97vvICjtD97/JIodO3YwadIkXn15Av0f+QsZoTUAWLFiBR9//LHz3lNTUwGIP3aIsIunSA+tiU0FEpF+nN4jX6btXR156qncmUXHcwStSDpmS1euXMmdd95JjRo13O5v9+7dtG/f3nnN9X+ZmZlJTEwMkydP9nmPqampZGRkFPgdyMjIIDU11ef3+mqQX/vF3TdXRGSW67lSajg6/vDfveStb/eCYDAUKWlZ2pwnwkUZDA60kJXj/wxfYkom5UICSc7I4aKZGTQUI/kqgyLia2feYWArMFcp9ScK6dtLKRWGDm+3C5jto+35wHzQsT19xcuMiYm5YvFmrwSX2n6FChXIyNCjxKZNmwJ6GXbkyJEMGzaMxo0bO/MeO3aMtm3bOtvxdEdQoYJ7mGiHDeANN9zgXFJu0aIFn376KZMnT2bcuHEA9OzZk5EjR1KxYkXWrVvnVkfVqlUJDw+nbNmylC1blqioKNq3b8/GjRvp3bs3MTExBdrnhYSEUKFCBQICAggLCyM0OIgc+0SdIybqxDEjtFJZRd9v9erVCQoKct6rY1buhmqVISmNCtXq6mXi00nUuqEGNpuNOnXqcObMGUJCQtxm8Rz/m8jISGbMmMEnn3zCgAEDnOkbNmygX79+DBs2jDfffJPQ0FBn2vz586levTpz5szJ9x4dfPXVVzz55JP5PouIiIgSG4e2uGPk5oeIfFRAlu5XpSOG65q0TK0Mes4MZln9H4skpmRSr0oY++KTzTKxoVi57C2UInK04Fy5KKVC0JtIAoG+IkXgD2TdZDgVfcWr9YfgyjfCQ29dVh2nTp0iOjqa/v3707JlS95++22ioqKIjIx0zvh17tzZrcyOHTto1qwZH3/8MQ888AA1atRg27ZtZGdn07FjRzZs2MCoUaOc+UNCQujfvz/9+/endu3abNiwgaZNm7JhwwYyMjJYs2ZNvn3s0KEDx48f5/HHH2fq1KnUqlWrQL9La9eudS4pA3a/gJqJEyeilGLH5lWkJKfQ5VGtDFqtVu87fR2h6Cy5abVq1uD3338HYMGCBSxfvpwdO3Y406Ojo3nxxRfZvn07n3/+Od26dXOrslevXmzevJk+ffqwa9cuVq1a5XQfkJSUhM3m/1e1TJkyfuc1XHH2F3cHDNc+aZlafrkpg4WwGbyYZSUlM4d6lcLtyqCZ0DYUHz6VQaXUd4WoZ66ILC8ok4siWB7oKiKphWjjmicuLg6r1Ur9+vX529/+xjfffMOtt96KxWKhc+fOZGRkkJSURLt27fLskgoNDWXevHm8+uqr9OjRg4iICJKSkhgxYgTDhg3jnnvu8bsfWVlZbN68mfXr19OkSRMaNmzIkCFDqFgx14NQfHw89913H9WqVXMqcp6mn0eOHKFx48YkJiZSpUoVt6XT7t3zTt4EBwezbeceXn/3Q2K79yUiIoLs7GyCg4OdeRxtqHI1ndfCw8NJPfwDjRvWZ+MP2wFYv349HTu6+0KfOXMmSil27drF4cOH87T/22+/ER0dzZYtW+jevTtRUVFOBXrixIlERkby9ttvF/j8Jk+ezIQJhbKaMBSAUqoW8IOINLSfVwEeFZEPPPKFAjFKqQEisroYumq4TnDMDEYEu2wgCbRgE8ix2ggMyN9sJiFFrwTVqaT3PpllYkNxkt/MYCfgKXIDwHsjHPgAeLqghpRSweh4xlXRyzgZduUQIFtErtwvoftrV6yqwpKZkkJh54R27drFwIEDOXjwIBaLhR9//JFWrVrRqVMntmzZ4rXMokWL3NyXrF27lrlz57J06VIaNmwIaPczLVu2ZODAgdx000156nDdidysmXaNsHr1asqXL8/ixYsZM2YMwcHBvPHGG6xYsYIdO3bQq1cvEhISaNOmDaGhoVSuXPmSvOZ/+umndLq1Edb0ZOq3ud95fdSTg3lj7kd88sknjBo1iqysLLcdwOnp6SilsJ05CjkXWb/rd/tSr4VbmjcjOjqaY8eOsXXrVqZPn+7W5kcffUTNmjXZv38/gwYNYty4ccyYMQMAm83G4MGDCQ8P56mnnmLnzp15ltsvXrxIixYtGDt2LACJiYk888wzfPTRR868b775pnO533BFCQTqgXNQuRqorJT6zCNCUhe0XPrl6nfRcD3hUAbDyrgvEwNk+aUM6p3Ede3KoFkmNhQnBS0Tf+krFB2AUqo88IGI5J1mycud5NrynPJIewJY7Ecd1yT16tVjwIABtGjRgt69e9OqVStnWmRkJIMHD3bL/9BDD+Wp48EHH2T48OFUrlyZcuXKoZTCarUSHh7OqVOnWL9+fZ4yhw8fxmazceONN7J69WqaNGnC9u3bnen33XcfzzzzDE2bNmX06NHOWbFq1aqxePFiTp8+zYIFCy7pnqtUqULtmtXIyQh3u14mOJiRg/uSnq7HIGlpaW42mAkJCdrZpz36yIULF7RdoMXCLTc1JSAggBEjRtC0aVO3jSCQa2940003sXz5ch5++GFycnKYMWMGI0aMYP/+/fz8889AXrtLB2lpac4dxg6fisePHycpKcmZbihy3gTqAh29yKdH0DOIJ65+twzXE6mZeTeQlLErgJnZNsIKmBVINMqgoQSRnzJYkI/BQiEiWzBuH7xSuXJlpkyZwu7du/OkVapUifr167td82WP5vCIfujQIWrUqMHOnTvp0aOHz3YdM25KKcLCwvK4TOnRowcTJkxg7NixLFy40M1lTZcuXViyZIlf9+cTm83N4bTujIWXx4+AGjcD2lbP1cnz7t279cynLRvKhHP+/HntLkdZCAyw0K1bN5YtW8aXX36Z7+7mrl27snr1agYMGMAXX3zB+fPn+e6776hXr16+XY6Ojubo0aP27msbn9mzZztnRx1KrKFoUErdBQxDr1zcp5R6B3haRE4opW5AO8F/ohi7aLhOcOwmdrUZDA7KnRksCIdbmXqVjTJoKH7yUwYV8KJSKr81r1C078GOIvL9le2a4WqSk5OTZ0naYrHw/PPPM2bMGM6ePXtF2ztz5gwn4kOxeg43PGITnzhxwrnsDbBq1SratW1Lemoy+3+PY8+ePTRq1AiUhYSEBI4dO4ZSijZt2gB6+bxbt25edzqfPXuWsLAwEhISAPjss8+oWbOmz9B8r732Gq+9lmuC4PAP6VC+DVeFn4F+IrJNKRUPDAR2KaW6AY+jYxN/6U9FSqkgtGurx9GOq38EnvG10qGUGgOMQbvc+g14XUQ+vsz7MZRSUp0bSNydTgN+bSJJSMkk0KKoUV5bS13MMhtIDMVHfsrgQrR9X0EsBK6tIH1XGZvNxtq1a0lMTMyzc3bMmDE+o3p4opSifPnyTtc0oF2znDx5kkOHDuUbf/eee+6hZcuWTJo0iYsXLzJ+/HiWLl1K3759mTlzJs8++ywbN25k4cKFbo6rvREbG0tiYiLHj+swS97aHTRokK+b4MKFZM5nxnHq1Cm+/fZb51L0kSNH+O6775g65SWysrLp+shAbDZhxYoVLF+7gWdfnkmNG2pz77330q9fP0aPHs2YMWM4ffq0Uxk8deoUy5YtY/78+fz+++9MnjyZMWPGsGLFCl5++WXef/99+vfvzyOPPEKXLl3c7BU9Z2gdYQNvv/32PMrm0aNHC3S1YygUQQAichHtlgoR+V0p1Qktg75Du7gaUQgPBZHAAPTS8u/AXOBrpdSfRSTLNaNS6i/AO0A/tIPrNsBKpVSciBRms53hGiHd2zKx3WbQH8fTCSmZVC0bTFCAhaAARUYh/BMaDFcar9qBUqqViAy3fw4HngP+ISJWpVQl4FFgoT3EnOEysVgsTJ8+nejoaHfXK8D06dPzxLl98MEHvdYTEBDA8ePH8/g5bNWqFTExMW6uZVx57rnnqFOnDo888gixsbFUqlSJxMRE5syZQ48ePQgMDKRJkya8//77hIeHe63Dlf379/PCCy+QnJzMQw89lMf+rmHDhsyaNYv2fyqHBJfjwy/X5bqlURbeXfgFb8z/jMzMTHr27Olc6rZarYwZM4a72t4BiQc5H/8rhFUCYNxT/2JA778wbdY7ZGdnM3ToUEaNGsXs2bMJDg4mPj6e+++/nwMHDtC8eXP69+/P4MGDnZFWBg4cSL9+/fjyyy+ZN28eDz/8MGFhYcTExHDgwAGvu58dnDiR1zwtMDCQTZs2lVhffaUFpVQAMA0Y6S1dRMTuhPpedNi6FYWo9ylguoj8YL82DDgJdEUbN20AACAASURBVANWeRRpAZwRkSj7+TdKqf3ArWhF1HCdkZaZg1IQGpQ76AsO9H9mMNGuDAKEBAZwMcsog4biI48yqJT6M/C9UqqCiGSjl0+mAq8ppWoD/4feALIcSLyanb2WcfWH58BXBIg9e/a4nRcUzc+xKcJXVIzx48c7P994440cO3YsT55evXrRq1cvt2uPP/44jz/+eJ68Dz/8MA8//LDP/vzrX//SH/7YjQoIdIaLA0AppowfzpRZc8DiPrN24403Mnv2bMi030dArl/D2TOnaDvC0FBCQ0NZvtzd01GtWrV45pln6NKli9OBt+fzCAwMZODAgQwcOJDff/+dX375hbp161K3bt0Cn7GhaLAPQFsD76LlkDemAyFoeTQaeMOPqhsCldDLzo62Tiul4tCzfp7K4CrgZaXU3SKyRSl1Izoue/4OOQ3XLKmZVsLLBLp5U3DdTVwQCSmZ1Kqgl4hDygSQaWYGDcVIHmVQRA7Y7QRvR8cedtAAWGc/xhSJs2jD9YPNBkjeDSTYN33kp3w5HU67OLn2sDX0xqBBg/yODuNQAg3Fj4h0V0rVA6ba/Qu2F5H/ACilXgbGomcGqwOLlFIfikhBMSEdJjDnPK6fQ4fb9OxDtFLqASBKKfUftHeEriJyyFvl/obT9KS4wwCW5PZLWt+O/JZJIFa3azFntEL3v59+Julo/mYi8WfTqB6QrsvnZBF3/A82b/b8Ovpu39e1q0lJbr84++YIm1qa8GVE9iOwRSnl+kbej35TDweGu4yGFHq1xoRcMPiPw62kxXM3sf17lZ9i51AGAzyVQTN7dx1wN/C5UuoxwAZMAnqJyA4ApdQJ4Blglu8q8sWrxwOlVE3gNXT4zA+Bv6GV00He3G/5G07Tk+IOA1iS2y9pfYv6YxeVMpPdroUdOwc7t3HTzbdwV2PfttU5Vhsp/7eOW25sQKdOTaj4yxbKV4qgU6dWPst4u/+S9kxKUvvF2beSEte9MPjyvfETsBE9O9gdLSBfRe+gO412Mn27/Wht/2sw+I/Nrgx6cS1jz5BP2Wydz1WRVJbcOg3XLCKyDJgJfAYcAu4QkW9dsnwEPOZHVQ4Tl0oe1ysCCV7yzwbOishMETkrIs+hN514ja1uuPZJz8xx2zwCrsvE+cuis2lZiJBrMxgUYCKQGIoVX8pgDFBTRPYA+9D+BqcDjdA78GYCzwJHRGSPPZ/B4D8+Zwb9XCa2eMRB9mOZ2HBtICKRwA50GMwDHslLgeZKqYZ5S7rxK3pJuLXjglKqGlDfXrcnf7aXceUI4Hsqx3BNk2a3GXTFX9cyCcna4XQ1F2XQ+Bk0FCf5KYONVe5asAJtzC0iC4DmaJubn+02PMWKMe4vhficGfRjmdiW475EDGCxAFIqlorN9/WSsOEeGnMMkKyUCnPNJCKn0SYt7oGpPbCHv/wQmKSU6qCUqoueVfwVWK+UmqCUOumiVG4CHlVK3aWUKq+UagOMAr6+EjdnKH2kZua4+RgE/13LOOISVytn30BilEFDMePLZvAQ0MjutiERaODqd0tEziilHkIv0Zy5Gh31RVBQEBcvXnTzB2coBVzuzGBQqI9yXqKalDAuXryY60rH4Bcichwo63K+F+jlI3t3EfnDj2qnoh3nr0DHM/4ReEBEspRSFnua460+CcgGlgA1gD/sn6d7Vmq4PkjLynGLPgL+u5ZxxCV2zAyGBlk4nW1WNgzFh9eZQRHJcQhT+67hPJqWaLbnKXyVqVatGvHx8aSnp5sZl9KEr5lB/JkZzM47M+iqDJZQRIT09HTi4+OpVi3PhlXDZaI0QX4qgohItoiMF5EqIhIqIp0d0UdE5A0RqSAicfbziyLynIjUF5EQEWkoIq/Y3W8ZrkPSMvMqg/66lnEsE1eJMDaDhpJBfhFIAFBK3Qr8qJTqIyIlbknEEY/3jz/+IDs7m4yMDEJCQv6fvfMOb+ss+//nlveIVzwSx85wmt3sdNGkSUvTveiilDJfWgp0UPgVePtSKLxAeUuBMgqUAi1Q6F7p3m5CR9Km2c12luMd7yHJkp7fH885lmxrWZZtyT6f6zrXkc58JB8f3ece33vExhPL54+psTnaoKsJmpN9ikYAtxPa6qBB9ff+gTb2Wmog1Q6pPuohzg7oPAZNu8Dm/7Lu+/lH4vtISkqiqKio57q1CB9DruVvQcTuzwUeEpFblFJ/H8ahWYxB2v0VkISZM1jfbic3PanHeEyzwsQWI0xQY1BEsoDHgX/HoiFokpWV1fPjWl5ezuLFi0dsLLF8/pga25s/hbW/gB80Gvl+BnW74Ikr4fK/wZzL+h+kYR88diV86s8w59Pe5TuegVe+AF97F4rmhD6/n/cWsYmRu1wC/BF4RETcaLWDtcAzSql3AZRSL4rIzcDvAMsYtBgy3B6FvdvTv4Ak3DBxq4PCcd4HUcszaDHSBDQGRSQNrbpfDdxi5A4Gi8OWKaXiT2nRYmRwtELKuN6GIECScYN0Ofzv11at5+OKei9PNtrkOTuxGHU8D+z3eZ+K7k98NvretBOtcvABuqjjjWEfocWYosOpndOBCkjCyRkszErpeZ+alIDDyhm0GEH8taNLRecIPolOnj4P6AIuD3Es61fYInzsrZCa3X95omkM2v3v116r55kTei83Q8rdHdEZn0Us8STwb3SrOZRSx9AC0D8XkalocefH0A+rbwJfGJFRWowZOhymMdj7JzTRJoiEzhmsb3NQVuDt856aZMPp9uD2KBJsfnXPLSyGFH+ewYuAfwLvAWf7VBG/PWyjshj9OFohxU/eXKLxtNwdwBhsq9Hzvp7BJKPGyfIMjkYa0PclgBuNqMVkYCYwH/3Q+hS6ZWY2uurXwmLICGQMigjJCbagnkGlFPVtjh7BadA5gwD2bne/Y1pYDAf+rrongOnA94DVwAvDOiKLsYG9BVL9GYOGhy+gZ7BGew9Tc3ovN8PE3ZYxOAq5D2hGe/5WolNXKtHaf1uALUoptyEa/SFaMua2ERqrxRig3aHz+zJT+stYpSTaguoMtnR143R7+uUMAnRZxqDFCNHvqjOkZO4UkU3oHqDfVko9ICJ3A1mAE/2kvhd4VSlV3/cYFhYhsbfAuIn9l5uewYA5gzWQWeQVpzYxPYOWMTjqUEpNBBARD3C5v17AxnZ1RgFJUMFpC4vB0ml6BpP7G27JiQlBw8R9NQaht2fQwmIkCNSBBKXUy8BlwO9E5Bygw5iSgCXAL4AqEblXRDICHcfCwi+OADmDIpCQAq4u//u11cC4Cf2X9y0gaTkKL/833LNAVyBbjDpEpFVEGkTkoIh8KCKPAE1KqVtGemwWo5v2AGFiMDyDQYpB+raiA0hJ0j/FljFoMVL4KyDJAFKUUo1KqbdE5Fq00v4yU4DVZ9uzgN+jK/tWD8N4LUYL9lb/YWLQFcWBPIPttVAw288+PgUkO56BJ7+i29ahoHID5B8XlWFbxBSZaG3BbCAXWIjuJpI7koOyGP14q4n9eQZtQT2D9e06BcZ/zqBVUWwxMvjzDJ4LHBGR34rIFKXUw8C96GTtXiilXgWWA78a2mFajCqU0mFifwUkoHMCA+UMttUGCC+nAqLFp8vvhPwZcMMHel1LZVSGbRFzKKXUK0qpx5RS9wEPEFz+ysIiKpg5g32lZQCjgCSwh6/HM5jlP2fQwmIk8Jcz+ISIVAE3A3tEZBu6Wu9cEflJkGO9NERjtBhtdHfq3sSBPIOJKf6riZ2d4GjpX0kMOrycnAF7X4X6XXDJH7VBmFEILUeiO36LkUIR3Njbhq4otrAYUsxq4r4dSMDwDAYpIKlrc5CenNBr37RkK2fQYmTxW7ZkKPq/a7Si+zOwDK3jZRl8FoPHbuT/B/QMpvn3DLYbsjJ9NQZNktKhZhuk5cG8S/Wy7BLLMzh6EKBZvMVDIiIuoBWoAN4FHkbLYllYDBkdDhc28YZ3fQkVJq5rc/TKFwRITTQ8g07LGLQYGYLWsCulNonIScAP0FINbyil7h+WkVmMXhyGMeivgAS0Z9BfzmCbITjtzzMI3rzBpV/wdjLJKYW6nZGP1SKWKO3zXtAC+YXAIrQW4ToR+a1S6lvDPTiLsUOHw01GciLSV9UAQuoM1rfZe+ULAqQlGwUkITqXWFgMFSEFjQypmTtE5GVg19APyWLUY2/R84DGYKr/amLTM+gvZxB0mFhssOzL3mXZpbD3NZ2naBHXKKWOBli1B/gP8HsRmQ70d9dYWESRDoeLdD/5gqA9g52droD71rU5mDOhd1QkxfAM2i3PoMUIEba6pVLqfX/LRSQZXWn8btRGZTG6CRUmDlRN3BYiTDxhPhQvhpzJ3mXZJTpHsbMx8vFaxA1Kqf2ht7KwGBztTldAcejkEKLT9a0OTpvR1zNoGINBCk8sLIaSsIxBEXlTKXVGgNUTgXVYT+MW4eIwPYNBqokdbf2Xt9WALQnS8/zvd+mf+3sAs0v03CoiiXtEZDe6mM0NuAAHuid6G3AM3ZXkA+B5I6JhYTEkdDhcfotHIHjOYJfTTZvDRWFWn5zBJCtn0GJkCWgMishcn7fLRWQO+iZ82NjP9AQmD93wLEYl9jByBv1VE7fXasFpP3k6PfRd12MMVqJl6SzimBnAt9Gt6RLQAvjJQAaQg26jeT3wGnD1CI3RYgzQ4XD57T4CkBIkZ7CuzdAYzOxbQGKKTlvPMBYjQzDP4Ha0jIP567rDmDejtQiPBz6DTt6+Z6gGaDEKMXMGB1pNbLaiGwjZRs1BSyXgR6zaIt54VClVFWiliJwMvDmM47EYg3Q43BTn+PeDBJOWqW/rrzEIkJhgIylBLJ1BixEjYDs6g3FKKRvgMuaF6CfxE9CCr48Ca4Z4jPFHxdvw4AXeHDeL3jhaQRK8LeT6ErCaOEArumCkj9fGpRUmHnWISLqIfE1Evu2zuBrdP93CYsjocLr8Ck5D8DCxv77EJqlJCZbOoMWIEcoY7JWApZRqAG4H3kBrfP0B+OkQjS0usbkdsOZGOLgOnvumVcXqD3srpIwLHO4NVk08UM+giKU1GMeIyGoRWS8iN+NzPxKRM4F9wP8Avr+sTcDdwztKi7FGhyNIAUmwMHGrjnhYxqBFrBHKGEREJvq8zlRK/RowwzTH0DdfC4PJh5+C5kNa9HjPS7D1sZEeUuzhaA2cLwj+q4ldDuhqCiwrE4zRYAx++Ddo2DfSoxgJNgOvAt9Fp6x8xljeBtwFTFNK/czcWCnVqpQK1inJwmLQtIcqIAmYM+gg0SbkpvcPMadZxqDFCOLXGBSRzxkvbwN+YizLANYaT+igw8S3Yz2Fe2k8wOTDT8Lxl8Flf4HSk+Cl71jh4r7YWwJXEoO3N7GvV7U9hOB0MLJL4jtM7HbB87fAhvtGeiTDjlKq3rjPTAa+CfyviLwAfKSUukcp1T2yI7QYa7jcHuzdnoAFJMmJNlwehcfTPypU3+YgPzMFm61/VCQ1yWblDFqMGP2MQREpBH6BDsnsA75nrDoeOALc591UbkffoC0A1t6NkgQ46ydgS4AL7gF7M+x8bqRHFlvYWyEliGcwJQuUR3sCTUJpDAYjuxTaaxFPnNoNznY9r989suMYWTzAn4DlGO3mRCQ5wBSk3NyLiCSJyK9FpF5EukTkdRGZGWR7EZEbROQjEekQkXYRWRGVT2cRN3QY8i+BcgZNAWl/eYN1bY5+sjIm2jNoVRNbjAz9jEGlVB0wxXj7uFKq3li+Xil1sVLKt8yzDCgZ+mHGCVWbaM45HrKK9fuC2bpQoq16ZMcVazhag3sGixfp+dGN3mWmMRipZxBIcTQMfN9YwNmh5w17RnYcI8tPgS60juCPAbvx3ncyl10b5jF/hJaguRRdat4FvGgI6fvjD8CNxn4z0PfJDyL4LBZxTKdTdxcJFiYG/ApP++tLbJKSlNDLM6iU4vp/buQv6yoGO2QLi5D4DRMrpRzo/JzPi8in8MrL9NlMfQm4dQjHFz+4XXBsLx0ZPt0vbDbILPT21LXQ2EPkDBYv0W3ljmzwLusJE0eYMwik2usHvm8sYHoG26q9sjxjBMN7Z95//ohWM0hCF40IMM1Ylmy8FqXUn8M4bgLwVeBOpdQ6pdQh4CvoB9xz/Gy/CPgScJ5S6lmlVJVS6lifh2OLMUCHQxuD6SGMQX95g/76EpukJSXg8DEGX9xWw8s7anh7T5zetyziimAFJBuALwB3AgkickBEficis4z1YhiK/W6cY5KmA+B20pFR2nt5ZpG3p66Fxt4SWGMQICUTCudBpY/Tpa1Ge1nT8wd+vqH0DCoF258a2rxQ0xgEaNg7dOeJTT4F1AKXAx6llNuYXOhUlm5zGborSbjl+2VAHtDjflZK1QIHgRP9bH8psAm4XET2icg2EfmBiITd0tNidNDu0AZbZqAwcYJhDPYJE7vcHo51OCkYl+pvt145g91uD794ZRcAVc1+lBUsLKJMwBuZUupk87WIdAO3oNX9twCnoZvD/wz9lG5RtxOAzvQ+xuC4CdBydAQGFKN4PKHDxAClJ8C2J/T2NpshOF2oXw+UrEkApNrrIhhwCCo/gCe+pPMSr3kKCgKmnEWOw8cYrN8NJcuif47Y5UXgGnQEYrKInGUsF2NaJSLHjGUFoIWnA/VS96HAmPdtWt2I1lPtyzxgDvAhcImxzf3oB+o7+m4sItcB1wEUFRVRXl4eYjia9vb2sLcdCmL5/LEyto+PaYNt78fbsdXs7LfdvirtOVz3zntMyPDer5rsHpSC5upDlJf3101vbbTT1OqhvLycNw53c/CYk+JMobKxg7feeouOjo5+nz9WvpNYPP9Ijq29vT30RjFGuE+17yulngGeEZGpSqmDGO0cRGQqYCU1GMn9nel9Uigzi3rnvo11nO2ACu4ZBCg5wZBT2Q2FcyLTGDRJSoWJC5l09AUtMZMdxTTXnWt0v2SXHf52ljYIJy2J3vHBmzMI+vsYQyil2oFXRWQa2ii83Wf1O8DX++zyLvqhNZQxGIhAxSdZwE6l1I09G4rciVZcuMPPuP8M/Blg2bJlatWqVWGdvLy8nHC3HQr6nv+Xr+4mIyWR61dOH5Hzh7tuODDP79hRAx9s5NSTlnH8pP7pLp3bqmHrRyxauozZE7z3uW2VLVD+H5Yvnc+qef0L4V4+tpV97XWccMpy/t9/3uLEaXmsnlPET1/cyZKTl7Np/Tv9Pn+sfCexeP6RHNtIGsiREpabRSm1wuf1wT6rq4HTozim+KR+J+RMxp2Y1nv5uAnQ0aBzCi20VxCC5wwClBiROjNvsK02snxBk8v+is3TDY9e47/vcSQoBTufh7KV8F+vgi0R1v4iOsf2xTQGE9OgfmwWkSil7lNKrQhz+mIYhzQTsfL6LM8F/LmQG4D9fZYdASJ8Qol9nt18lN+9uY81mwN2/xuThCwgSfCfM9jTlzhAzqApOv3X/xygod3J986dzcQcHVKubrFCxRZDS1BjUETKwpBpSESHTsY29bt19XBfMosABR1DEKKMR8wCiFBh4vHTIS1Xh2HbaqCxwlulHQn5M9g1+5tQtQle/HZ0OsPU7tC5onMuhLwymLQUmgehZ1i1SX/Ovjjb9HziwjHnGfSHUVRy2iAPU4EOCffE3A1ZranofOm+bANONgpPTMrQOYajjgMNHdz21DYAGjus7n6+mDmD6UHa0UF/YzBQX2KT1KQEOp1u/ry2grPnFbFkci4Ts7VzobrZqlOyGFpCeQb3AieJSLDt7gNei96Q4hC3Syf2F8zqv87spWsJT2vshmcwVJhYRIeKj6yHp67VuoMnXjeoUzcUnAwr/h9segg2PjioYwGw63lAYNb5+n1WMbQOotPJU9fBaz/ov9z0DE5aCk0Ho+fZjBNEJFdEfFv5FAJvDeaYRsHJfcB3RWSFiExG5wBWAC+LyLdEpFpEyoxd/oj2Iv5eRIpFZCk6j/GPgxlHLOLxKG58+COSEm1cvKiYxg4nymqr2YNZTRxKWqa/Z1AbgwWZgauJ3R5Fp9PFrWfr35JiwzNYZXkGLYaYUMagoPNyukRko4j8UkRO6lkpchO6PdSdQzjG2KfpILgdUDCn/zpTJLnd8gwC4YeJQRuDDXvgwFo4/24o9ON5HSin3wbHnQkv3kpWyyC9bDufg8mnQKZRi5A1SQtlOzsjO157HRzz4xk0C0iKF2uj+NiYa0uXDlzWZ5kYRmJenynEU0Yvfgg8DDwN7AYy0NIxTvS9MQ0tdo1SqglYjRbfrwAeR+cE/mEQnysmWX+gke1HW/mf8+YwrzgLp9tDu8NKczHpcLiwiTbe/NGjM+juHybOTU/qWd+X1CS9/PKlJRxXOA7QhqNNLM+gxdATTs7gYuAM4CH0jXCdiHwoIj9Hdyr5L6XU2G6xUa8lAPyHiY3CREteRmMfgDFYauQNLvg0LPpsdM5vS4BL74esYubt+HnkRnpjBdRu1yFiE6NqmdYIcqw8bh1Cbz7UP4TtbIekdK8xbIWKTRrQuX++U5OIPBXOzkqpbqXULUqpfKVUmlLqTKXUHmPd3UqpHN8caaXURiMnMVUpVaaU+rlSatS1jHh281HSkxM4f8FE8jK0F8sKFXtpd7jISE4kUAZVwJzBVkfAfEGAafkZ5GUkc/OZXkWCxAQbRVmpVLdYxqDF0BKOMdiglHpHKfVrpdTZwBJ0Xs2twItKqQeHcHzxQb0hL+BPVsSsgLWEpzX2Zj0PFSYGmHqaNtwuuEeHjaNFeh5c9S8SXe3w+BfBHUGbup3P6/ns873Lsk1jMAIpIXsLoLTh19lH7cTZDsmZMP44QMZsEUkfFDDTz3Q1cMoIjiuu6fYoXtxWzdnzJpCenMj4DN2M5ZhlDPbQ4XCRESBEDJASJExcGEBjEOCseRP48H/OZFJO7yLEidmpVgGJxZDjrzdxloj8RETOQd9wlbE8RUS+CryB1hq8EThXRO4azgHHJPW7tc5cyrj+6xKTIS3P8gya9ISJwzAGbTZYcCUkp0d/HBPms3vWN+DQO/Dq7aG378vO53RBR+4U77KsQRiDvn2Ymw72XufsgOQMSErT57M8gwAopfb3nYDDQNtIjy1e2VrvptXu4uJFulgrzzAGG9stY9Ckw+kO2JcYgheQBGpFZ2Kz9X/onZiTZnkGLYYcf57BCejQ8D/QOYP/FpHfApXAN4AblFKfVEr9Aa3K/y2jE8nYpW6X/+IRk3ETLM+gib1V6/IlBn5CHi7qilbBSV+D9X+ErY+F3L6H1mqo3NA7RAzeaudIRMZ9vYHNB3uvc3boriwA+bMsz2Bw6rFaZEbMe1Uu8jOTWX6c7vTTYwxansEeQnkG8zNTEIHKJq83TylFfZuDgqzgxqA/irNTqWrusop4LIaUfsagUmqPUup8pVQhsBTtCfwkMB54Wyn1uM+2LwK/Av53mMYbe9hbtaemaF7gbayWdF7M7iPRDPsOhrP+F6acCmtuguqt4e2z+wU9n93HGExKg/TxUfAMHuq9ztGmw8SgUxGO7dM5hmOLsC4YpdQ+pdSzQz2Y0UirvZvN9W4uWFBMopH3Nj7TChP3pcPIGQxERkoiZfkZbDva3LOspasbp9sTsJI4GBOy03C4PLRHkM0SK9S12tlTaznsY5mgOYNKqU1KqZ8opeYBq4D1fjb7PnBuuCcUkVQR+ZOIKKN7SXyz+yVwO73yIv6wPINe7K3hFY8MFwlJcMWDWtPw0Wv65+v5Y+dzMH6Gf29wVvHgjcHmPsagGSYG7Rl0O/qHkkc3Tei0FF9i5Gli9LB2Tz0uD1y40Cvunp6cSGqSjcYOxwiOLLZod7iDegYBFpTksO1oS8/7uhAag8Eoztb7NNrjt1bpV6/t4UsPfBB6Q4sRw68xKCIeEXH7Tmhdr3/4WW4nTOFVEZmDFnQtDbVt3LD9Scgq0TIogcgsgvba6Agdxzv2lvCKR4aTzEL49D+hrRqe/Epwr1tnIxz8D8y5wL93M6sksmriLsMIzSvzkzPY7uMZNAzQhrETKlZKdSql7vVZZMPqiR51thxpJtGmDRlfxmekWJ5BHzocLjKD5AwCzJ+UTW2rg7pWnevXIzgdImfQHxONgpJGe/z+fjR2ODna3NWj0WgRewTyDJYCk/1MpcAU4K/AAz7LJod5vk8Af0HnHsYOlRsjkxjpaoL9b8K8S3SxQyDGTQBPd3hep9GOGSaONUqWwbl3wf434K2fBd5uzyvgcfXPFzTJnqT7Hw+UriZAYMKC/mFiZ4fXGMw3KtZNOaMxgIjcIiJnG69PA3YBt4zsqEYfW460MDXLRlJC73tZXkaylTPoQ6czeM4gwPwSHf0wvYNmK7pIjEGvZzB+jcGubv2AfaChI8SWFiOF3ytaKRU0ziUijwO/DbWdn+P+1dh/6kD2G1K6u+DB82H+5XDx7we2787ntZF3/KXBtzPlZdprIGN8ZOMcLdhbdau5WGTpF+HoRlh3txZ4nnNB/212PqerhouX+D9GVrGWz/EN7YZDV5MOn+eV6c4mHrfWRATtGTQLSNJy9PU0topInMCTIvJX4JvAJcA/ReRzwDF/OyilzhjG8cU9LreHbUdbWF7c/6HWMgZ70x6igARg7sQsbAJbK1v45Jwi6lqN7iMRGIP5mSkk2oTGrvg1Bjud2hg8eKyD4yfFUJqQRQ9+r2jD2At25aUAM0WkVCk1iGasgRGR64DrAIqKiigvLw9rv/b29rC3Bcht3MxCVxcdu8v5YAD7ASzY8hfSUiewfk8r7C0PeP7s5moWA1veeZWmvPoBnWOgBPv8A/1uok17ezv21jqaEorZPQLj6Pv5/X0ftsyLWDTufdKf+Aobl95NV3qJd53bzql7X6N64mr2vf2233MU1bQxB1j/+tO99g01njkHd5FFKofrHMzyuHjv1SdxpBaCUqy0t3G4uoEDxrYLE4tIqPiAjwb5HcbyteKLUupeEVkHvAQUKKWuFpET0MVthEwbbAAAIABJREFUJcCPRnSAo4B99e10dbuZlt3fWBmfkcy+uvYRGFXs4XJ7sHd7ghaQgC4imV6QyXbDM7i7po3MlMSALeyCYbMJRVmpNNrj1yA3jcED9ZZnMFYJdGVuD2PfdUB1FMfSC6XUn9Htnli2bJlatWpVWPuVl5cT7rYAvKZbnGZ0VrLq5CXhhzA7GuDtbXDqTaw6/fTg5z9WCptvY2HZBFg0gLFFQLDPP+DvJsqUl5eTqhxMnDqLiSMwjr6fP+D3sWwu3LeSkyp+C9e+4dWP/PhZ8DgpOfNrlExb4f8kBxJg1685adYkmO7n2IHGc+S3kDSJWSethj33csqsiTBthe5D/LaHKTPmMmWFsW3HSbDlEVatXDmoquxYvlb6opTaKiKnA+UicqpS6h3j/XqgRin1xggPMa7ZckRXvpZlW57BYHQYRk0wnUGT+SXZrNvbQFVzF89treLqEycH7FoSiuKcVBpb4reIp8upcwWtMHHsEihMPHaetCvKdauv7k6o+gjKVoW33841oNxwfN+WqX4YZ/YnHuPyMspthDxjMGfQl+wSuOIB+Mcl8MzX4Mp/aqNr53NaOmZykAYXkXYh6WrSnVFMEevmQ8AK/X0BJPsImhfMAmebLngxtQ3HAEqpPSIyTSnlMN4fFZHFSqmhdbePATYfaSErNZGi9P7GSl5mMl3dbrqcbtKSQxtBoxmzACIcD9+CSdk89dFRfvLCxygF155WFvF5J2ancaC2GaVUxAblSNLjGTxmGYOxSjjt6EYvnY1QvUXnigFUfhj+vtuf0vIiRceH3jY5Q/+Yj3F5mUSXIcIaiwUkfZl2Gqz+sTYA198HLqcuHpl1LiQE+SEYZxhnA60o7mrS8jbZpSA2b0VxjzHok3/YU0Qy9jqRmIagz/tehqCI5InIwBOzxjhbK5tZWJrj19DwtqSLX89UtDCNwVA5g+AtInlxWw2XLJ5ESW7knZROmJpLQ5fiiY0RFKfFAF1Oq4Ak1gl6RYvIbsADuAEX4AA60e2ejqG7knwAPB+XDdsPrgMUzL0E9r6qiwfCoa1Gy4us/E74YbqsiVD5ge6DmzA2VTESXcaNIJZ0BoNxyjfgwFp47Qe6UMjRCnMuCr5PUiqk5w+8orirUbctTEjS8jRmRbHT+M58jUFfeZnppxPTOHyKXyJEROYGWNUEnAQUKaXuM5b9FUgHzh7USeOIV3bU8M6+Bn58cRgPpn6wd7vZVdPG9SvLgP49cPMytG3d2OEclEEzGhhImHjuxGxsopPvr185uKK5q0+awkNrd/LDNTs4YWoeU/MHUJw2wiil6Ox2k5aUQHNnN00dTnKNBwyL2CGUZ3AGOm/vl8DvgQeBZ4D30W2fpqNvvg8N3RCHkIpy7bGbtAQmLdOeQaW0V+bV74M7gCbSx88CCuaFqCL25RM3wdEP4YVvjVm9wR5jMNbDxCYiusI8ZZy+HpIzYdrK0PtlTxqYZ9Dj1vqLabn6fe4Ur/C0w/AM+hpUmUWQkh37nsGKcrhrWjQqn7cD2/zMf2qsv0dErhKRG4CLgT8M9oTxxMMbDvPP9w9h746sK82OqhbcHsXCPvqCJnk9nkErb7DHMxiigAQgLTmBZVPyuGTRJI4rHNwDUYJNuG6Briq++ZFNdLvjx/fidHtwexSzJ+pUFytUHJuEEyZ+VCn1gFLqL0qpPyqlfqOU+plS6jtKqauBC9FSD2GjlDqolBKl1MFIBh01Ksph6nLtjSlZBh110HJEtyZ793c6hOyP7U9B4VwonB3+uZZ8DlZ8Gz76B6z7ZVSGH28kuDv1i3gIE5tkFsLFht7xjNXa8xeKrJKB5QzajU4FvsZgvzCxz4+JiG5LF+vC09VbdXeebQPo+xyYUqWUDS1aXwD8F4BS6hngBvRD6a+B746ldnRKKbYcaUYpOHSsM6JjbDmir79Fpf6NQTNM3NhuGYPtAwgTAzx83cncfcXCqJx7fJqNOy9dwJbKFu55PXr/+0Pd89gMEc8r1vd9q6I4NhlQzqCIpIvI10Tk2z6Lq9E6YPFF0yForIAyw9Mzaamev/4jOGDIhtTv7L9fSyUceX9gXkGTM26H+VfAm/8LW6PyAxlXxJ1n0GTWObqI5Mw7wts+q1hfJ+HeZE0x8vQ8Pc8u1R1rXE7/xiDotnSxLjzdfFjPtz8VDW+48pmbU5mI3AX8GKgBbgIKRWTM5EIfbuykqVM3ra2oj0z+Zf2BY0zKSQvYKi3P6E9sVRQPrIAEtEcvwRa9go/zF0zkiqUl/KF8P+9X+JXYHDC3PrGVLz6wAadraLyNZvHIrAlae/Gg5RmMSfrdNEVktYisF5Gb8dEaFJEzgX3A/6B1Bk2agLuHeqBRxzT4ylbpedHxkJAC25+AgjmQmAp1fozBHc/oeSihaX+IaC/T1BXwzNfhwLpIRh63JLpMz2Cc5Az6MvciyJ0a3rbjp+v8wo4wi1zNvsSmZzDLqEhuq/KfMwjaM9hRH9tdbVoMCdLG/VCzdbBHkz5zgFlAKvB1YCbwJ+DTQJBG4aOLzYYkDEBFBMn59m436/Y2cPrsgoDbjEtJJClBrDAxAysgGSruuGgeU/LS+dajm+noHtxD1rF2B09vOkr57np+9qKf37sBcLChgwfeOdBvuWkMZqUmUpqXHtF1ajH0+HuC3gy8CnwXfeP9jLG8DbgLmKaU6unXpZRqVUr9ZKgHGnUqynXuVYER6k1MhomGO/+8u3TFpj/Py46ndMuwSLtoJKboPrh5ZfDoZ6Euxr07USTuCkgiJX+Gnocbxu1nDPpUJDuCeAYHco6RoPmwluGxJWrvYOSYnkDoLYb/klLqJqXUs0opt9LxrieAKwZzsnhi0+Fm0pISyM9MoSKC8Nt7FcfodLo5c05RwG1ExNAatKqJzQKSSMSjo0VGSiK/uWoxdW0O/r7DMagw70vba3B7FGfOKeTBdw/yzKYBSmL58NSmo/zouY9p6vPQYIaJ05MTmZafYYWJY5R+xqBSql4pdTu63/A3gf8VkReAj5RS9yiluod7kFHH44GKt7VX0Lca+BM3wOnf17IihXP6G2pNB3XFcTjagsFIy4XPPq49kf+6YsxIzvR4BuMtTDxQTEMt3AKPLsO7ZxqD2Ubnkpaj3jBx34rcggGeY7hRShuDExfp/7MdgwoVC7BJRKqApcAO4DfAVSJS5TsBXwZOjcIniAs2H2lmfkk2xxVmcKBh4GHi1z+uJSM5gVOmB2+TmZeRYoWJ0Z5Bm0Bq0shmIiwszeGW1TPZUOMelNzMmi1VHFeYyR+vWcqJ0/K47eltERcitXTq66OyqXdFeqchOJ2enMDU8RkcPNYx5HmKFgMn2BXtQYddlgPvAYhIcoApvlQw63ZAZ0N/gem5F8PKW/XrgtnQWulN7gfY8bSez/vU4MeQOwU++5gex7+v8HqARjEJ7g4dfk8c5bICWZMgKSMKnsGjOkwsNv29+ZIzWS+LVc9gV5M2ZHNKdX5t8+HwpZv6sxr4LPA54DzgGuAydPHatcZyc7oMOHeww48HHC43H1e1sqg0h7KCzAFruCmleH1nLafNLCAlMbhUyviMZCtMjNGXODkxJoSfr185nVm5Nn64ZgcHIwi9Vrd08cHBRi5cUExSgo2rTiil0+mmusUe0Xha7droq2zqXcjUaRiXackJlBVk0Ol0U9dmeZljjWDG4E/RolMfoBO07cZ738lcdu3QDjPKVJTreTCZkMI5eu7redn+lJagMbtEDJbixXDFg7rq8r3fR+eYMUyiq3P0h4gBbDbIP24AnsEmQLzfTco4LR3TangGkzP761naErToeax6Bs3ikZzJMPt8SEj25ukOEKPV3HvosPAbwPVKqTeM12uA94zX3cB3lFIxaiFHl53VbTjdHm0M5mfQZGi4hcv2o63UtjqChohNrJZ0mg6Ha0TzBX0ZrNzMC1urUQouXDgRgOKcNACONvXXmgyHli4dNOzrGfSGiROYZugjRpLSYDG0+CsgSfLx9P0RSAaS0EUjAkwzliUbr8XoIxw/VLytcwLN1mH+MHMJzSKShn06CT6SwpFgzDxbj6V60An2MU+iq2P0h4hN8mdBw97wtu1shLQcbeCZmFqFpjHoj4KZ0BAHxmBaDty0SUsrRY4A5hd0iYjMEJFZxvIZIjITLYS/WESi/E8am5j9hBeV5nh/ZAfgIXptZy02gdNnF4bcNi8j2ZKWAToc7rAEp4cLX7mZ37we5v3G4LktVRw/KYuyAn1/mWQYg1XNgzUG+3gGTWMwKZGp4/V1alUUxx7+PIOfAmqBywGPkZjtVkq50Mnb3eYydFeS+Ar+u5xw6J3QPYhzpuiexWYRyQ4jAX7ugCQVwyN/Ruz+qEeRRFdHfGkMDoaCmTrNIJzwv9mKzhdTnsbR3r+S2CR/FjQfAWdk+nJDillJnDNZz808yAEiIsUi8jiGrqAPbwCvo+8/LxjvX0Z3SSoN89hJIvJrEakXkS4Red0wKkPtVygih0SkfCCfJdpsPtJM4bgUJman9vygDyRU/PrHtSybktcjKh2MgnEptDlc1EQYQhwttDtcI1o84g9Tbube8n2sD1Nu5mBDB1sqW7hoobe3eVFWKiJwNEJjsDWgZ1CHj9OSEyjOSSM50Wa1pYtB/BmDL6Jzcg4Ck0XkLGM6G/0UvspcBqwCEJGTh2m8g6fyA+juDG0M2mzaY1e3UxuQmx7SlZHBvImRUjALGg/o84xiEl2dY8szCOHl9Pk1Bk3PYEfgdm4FMwEFxwbmERgWmg/r7j6p/oWMB4AH7fH7CYCIfBmoVEpNVkqZRt9MpVSpz/SbMI/9I+Bq4FJgNjrl5UURCWgdiUgSumL5UGQfJ3psPtLMIqOfcEluGok2CVtr8GhzFx9Xt3Lm3NBeQYDz5k8kNcnGd57cOqaT/zudLtLD6D4y3JhyM7c8upmWztA1ns9v1R2Szl/gNQaTE20UjUuN2Bg0PYNHAnkGkxNIsAlTx6dbYeIYxF81cbtS6lX0DW88cLsxfR94B63pZS67HnjXmMcHFeU6IX/q8tDbFs7RnsEP7tftwVb8v6EZU/4sUG4tgj2KSXCPkZxB0A8SMABjMK/3sqxJuiNOV2PgMPFAq5aHk+bD2is4yER7pVSNUup6YBJwD7qo7bs+m1ShDcYBISIJwFeBO5VS65RSh4CvAGXAOUF2/T2wF3hzoOeMJs2dTg40dLDQ6BqSlGBjcl56j8flo8NNPT/O/nhjp1Yw+GQY+YIA0/IzuO28OazdU89D6w8PcvTxS7vDHTM5g774ys3c9sy2kAb7mi1VnDA1tyc0bFKckxqFMHFXr/ObxmBakg6vmxXFFrFFwAISpdR9SqkVYU5fHMYxD46Kct1tJByjpGA2tFVD+c9h+hkw48yhGVOBaTjE4I96FBlTYeK8MpAEbQy6u+Hfn4Y1N2oPcF+6Gvt7Bk0PdMO+wGHi8dP1g41pDI6Ex6alEp67WYerfWk+4g0RRwGlVJtS6tvAIuBZn+WlSqlIYpdlQB7QU+KslKpFR0RO9LeDiHzdOP/XIzhfVDHFphf7tJArK8igor6Dl7ZVc+kf3uWv//FzrRm89nEtZfkZTC8Iv2fu506ewooZ+fzshZ1jNszX4XCRGUM5g76YcjMvbK0OKjezu6aNPbXtXOgTIjYpzkmLyBi0d7txuDzkZybT6XT3dMUB6Op2k5pkw2Z0YplWkMHhY524PWPXwxyLhPWIY4RGTlFKrR3i8Qwt9hYtb7H8lvC2L5yr5852OGsIdbVNL1L96C6CHFMFJInJ2iCs3w0bH4Q9L2vx5U3/ggVX9i6mCJQzCOBoCewZTEyB3Gn6IcLdDfefoR9aVv9oSD5SP5oOwd8v1F5zZwdc9hfvuubDMOWUqJ9SKfVxlA5lttzo28KlEegXOxWRlcB/AycrpRyhpEVE5DrgOoCioiLKy8vDGlR7e3tY2z6zz4kALQe3UV6px5LY5WBfnYubH/kIgPd3VFCeVNVv3y6X4t19nayektTvXKHOf+kkDx8ddPOV+9dy20mpUW21Fur84X43Q0V7eztN7ULLMeeIjMPf5++7bA6KWbk2bn96K57avRRl9Pf3PLlHXzs5rQcoLz/Ya527zUllUzdvvvUWtjC8+ub5mx3aOT8h1U1DOzz7+jqmZWujed8BB0l4esbpbOjG6fbw1MtvUZA+OL3GWL1e2tvjTyrOrzEoIrnAfUqpK41FhcBbeKv54pOD7+hwbNmq8LYvMozBxddA0byhGpX2/GSXjm7PoLubBI9z7ISJQeeCVm+FI+thynJtLL37O/jwb7DlEeYULofZ+fohJb1vmNin4CKQZ9A8R/0ebXDWbAXU8BiDjQe0IehohdkXwLYndBpF4WzoatZGbBQ9gyKyFrhcKVUXtYMGOJWfc08B/g18WikVVosGQ2HhzwDLli1Tq1atCuvk5eXlhLPt3w9sYEZRF+ee6ZXHqk4/zMsHt1GYmUrBuBQ6lWLVqhX99n1hazVu9RFfPnsZJ07rfd2Fc/6EiVXc9PAmdlLCDatmhPW5wiXY+cP9boaK8vJynJ4uZk6bzKpVc0bk/H0/v79lsxd3cc49a/n3wRSeuP4UkhK8BpdSih9sKGf5jGwuOvukfuc4nHKQlw7s4Phlp1A4zn+van/n31fXBm+tZfm8qWx/ez9FZXNZNV9L1qyp20xWW2PPONMqjvHAjvcpPG4+K2cGboMYDrF6vYzkQ0ukBDLL09Hirb6IiOSKSF6fKX5cPRXlkJgGpX6jQP3JLoHPPwvn/HxIhwUYFcWj2DNob9XzseIZBP03bTmsewiv/jFkTYRzfgbf3Aan3kx+wwfwJ6NZRiDPIGjdwUAUzNK9f8vv1O/rdoErQkHXj9eEJ3F0bD88eL72mH9+DVz4W22wvm38n/StJI4Oy9F9iBGRw0GmChEJxxo2G0f3scLJBfoanF8EioF1IqJERAE/BFYa71dF+qEiQSnVUzziy0nT8pgzMYs/f24Zi0tzONjQ6Td37PWdteSmJ7FkcmTFPRctLOaCBRO55/W9bD/aEnqHUYLbo3C4PDGZM+hLcU6alps50txPbmZrZQuHGzv9hogBirNNeZmBZV60dOmK4XnF+v5+pNFbRNLldJOe7PUjTSvQD7cHwix2shgeBuqjbUDfRH2nJhEZVOPRYaOiHKZ8QofXwqVsVXDPTLQwdek8A86Fjw/sOsdpTHkGzQKPuRdDyVLv8swCWP0j3jvlfjjtVm00FS/pvW9Kpve7Cnb95c8Cj0trFZ76TfB0e7UxB4LbBU9/VefHBqNhrzYEXXb4wnNQvAgyxsNJ1+sOPbU7vBqD2WEpvERCCXAjugDkdrTovTn9BghH0LACHRJeZi4QkUJgKrChz7b3AYv7TPeh8w0XAx9G/Eki4HBjJ02d3Swq7f0AUVaQyUs3r2B+STZT8zNod7j6dQ1xuT28uauO02cXkpgQeYjuJ5ccz/jMZG55dHPE7cviDbvxMX0Nm1glkNzMmi1VJCfYOHveBL/7FUeoNWjKypTkppGVmthLXqazjzFYkJlCRnICB4/FoCTWGGYgdwMFzPQzXQ1EPzko2rRW6TBs2aqRHol/CmZqyZvWyPtM9qOrWRcuRGIcRBuH4RkcKwUkoHtcT10BZ97hd7UrKQvO+L72FJae0H8DM1QcNExs5JsuvAqWfF6/rt6i502H4B8Xw7PfgPX3edve+aN2u77+arcH3qZ+tzYE3d3whedhwnzvulO+obum/OsK2Pa4XpYTpU49XtaIyEnoe9FrwOnA15RSr5gT8CiwK9hBAAyd1PuA74rIChGZDNyPNhJfFpFviUi1iJQZFc2bfSegBmg33g+ri8MsHllYGvjBqkfct0+hx4eHdJXx6jCriAORk57MXZcvZG9dO3e/MorTW3ywu7SXNdZ0BgNxx0XzmJSTxp0v6X8Hj0fx/NYqVs4qIDstye8+kQpPm5XEWWlJlOSm9xKe7nS6SPMxBkWEaQUZAxJIHyme21LFoTFS+TygR0Ol1P6+E3AYaBua4UWRA0btS9mqkRxFYHpkQqIYKt7ysC5c2Pyv6B0zGPaW3r2ce60bg2Hi7Enwxed1IUkkmKHiQAUkABMXw9l3wlk/1cUkKVleY3DLw7rbzu6X4aXvwEOXQXeAm3zlB3refMj7t/Kl9mNtCCoFX3zBm09rkp4Hn39aG647nta9mfvmQQ6e/YBZxHYaWoj6GhGpMifgTaXUsoBH6M0PgYeBp4HdQAZwnlLKib43phGBbM1Qs+lwM2lJCcwqCpw+MDXf7PTQ2/vy+se1JCfYWDHIXC2AlTMLuObkyfz1nQO8tz88seN4xvQMxnqY2CQjJZHrTitj85FmPjrcxIaDjdS2OgKGiAGy0hLJTEnsJxwdila7Ngaz05IozUvz4xns/Z1Ny8/kQENsh4m73R5ufmQTv35tFKdv+TC4Uh5NPXBrFI4ztFSUQ/p4KDp+pEfinwJTpDhKT9lKwYcP6Nf7hkkS7aHL4ZHP+l83Fj2Dg8WUlwlmDNpscMrXdajWZoMJC4xCEmD3i1B6Ety6D678Jxz9CJ6+3n8qwhGfyGjtjt7rarbB3y/Q1dBfelEXifhj0lL46jod+j75a4PWGDTxaY95C7AEXeTRDXxVKbUPyAQuBC4C/Me//KCU6lZK3aKUyldKpSmlzjT7Giul7lZK5SilDgbY9w6l1KqIP9Qg2HykmfmTsoOGeUty00iwSS+vhlKK13fWcsr08VHzbt123hym5KVzx5odoTeOc+LNMwhw2ZISxqUm8rf/HOC5LVWkJSVw5pzAQuMiEpHWoCl0nZVqega9WoNdTncvzyDAtPHpHG3qwuGK3RSDujYHHgVr9zbgGQMyOMGMwbDu5EqpfUqpZ0NvOcIc3QilJ+sfzFgkI18LD0dLQPjQu9qwLDoe6nZAW010jhuIqs1QuUGft6u5/3rTYziWcgYHS5ZpDA4gZ3XiAqjZrkPE1Vtg1rnaKJt7kS5i+fgZKP9Z//0qP/DmLfqGiqu36KrhxFTtEcwPUT2alKpD35+8PfwxB0FEJgFbMNpeKqVMq+M9pdQTxmuXUmqjUmpYc/dGAqfLw8dVrSwKUfyRlGCjJDetlx7g/voODh7r5My5gwsR+5KenMiFC4vZW9dGtzvmnKhRxa5rJOLGMwh6rJ85cTIvba/huS1VnDm3KGQHleKcNKpaBh4mTktKIDlRX3dd3e6efNVOp5v0pD7GYEEGHtW70CTWMFsvNnY42ToGCqUCWUZN6ARtX6IrKDWceNzQdBDyjxvpkQQnf2Z/r0ykfPg3bXid/yv9fv8Qewc3Pqjnyu0NyfsyFsPEg8U0BgO1o/PHxIXg6tISNgCzzvOu+8SNsPhzsPYXsOVR7/L2emg6APMu0e3jTGOwsUIbgsmZ2hAcP31wnycy6tAtMoFeXsIJIjLVZ3GBiETPyolRdla34nR7+lUS+2PK+AwO+YSJXze6jgTzDEVCaV46HjXwPLN4w+7W3qF4KCDx5fOnTEEpRavd1asXcSCKc9I4GkGY2MxDLMlNB7w9inULvz7GYL6+p8VyWzrfPtzlu4da0Wrk8WsMKqU6lVL39tnOf8ZpPNByBNxOyBuRH7PwmbEajn4IVZsGd5z2evj4WVj4GSg5ATIKhtYYdLTpooH5V+p+tP7O5bCMwQFTvFgb9APJOZy4UM8/+ru+3n09eSL64WDqClhzA9nNhn6zmS9YcqIuCqkxjMGND2ox6S88B3nTBv1xIsEI5X4P/TB6ObAN7SX8JLBBRJYB2eiCjuoRGeQw4i0eCW0MThufzsGGjp5w3esf13L8pCwmZqeF2HNglBo//kcaR7kxGIdhYtDG2bnHTyQ3PYnTZuaH3H5SThpNnd10Ol1hn6Oly2sMmkUopkHZ1e0mrW/OoFngFMPFGTWt2hicOj6d8t31IbaOf/wagyJyi4icbbw+DV2dF2bbjhjk2H49HxnPRviceK3+8V979+COs+NpLTGy9Is6LD79DG2gqSEK42x7QmvOnXitrqDd/0b/1mj2Vty2VEiIrxvpiFI0F753eGB6feNnaC1NtxNmn9c/by8xGa78B+RMZt6OO7V4dOUGnQ9YvMhIK/hYe3h3PK0LrkbIEPTDd4E/GK//BdwFPInuoZ6MfmAN/WsXx2w+0kzBuBSKs0MLAk8Zn0Gbw0Vjh5Nj7Q42Hm7izEFWEftj8nhtDB6O4ZBfNIi3AhJf/u/yBTx343JSEkN7Nb0VxeFrDbZ0dZOVpr+XCca1Wdtqp9vtodut+nkGs9OTyMtIjum2hrWtdpITbVy8aBJbKptp7CPTNNoIFCZ2Ak+KyG+AdcAlwP8TkS0i8qa/adhGHAmNFXoe657B1Gyt17br+cGFiw+/p2VJCg2V/OmfhM5jZLZXRGecvigFGx+AwnnaCzn9dK0z19jnXPYWXInDoNc41klI9HbL8Q0R+5KeB1c/hiiPlh6qKNcewaQ0vW93J0W16/Tfcd6lwzb0MFihlPoD2ks4Gx0+/j26aGSWMc0WkbmBDxHfmGLTodrhAUzL93pfXthWjVIMiTE4ISuVpAThSNMoNwbj1DMIesxm+DYUptbg0QGE/Vu7XD2ewdz0JJITbNS22el0agvaX2h9Wn5GTBuDNS12JmSlcvrsQpSCdXtHt3fQ71WtlLpXRNYBLwEFSqmrReQE4A204OswNT+NEsf2a6mLcWEXGo4cJ10P792rvYNXPBDZMY5s6N1lZfrperb/7/DSXp1DqdzaU+jxnbu983HFcPZPwRbiSfL1H+oig4t+p71Q08/Qy/e/2dsT62jBlZjOAOS+LSJlyie0rmZJkE4746ezY973WLTtDi1afeJX9fIJutp+6sGHISEZZp8/9OMNH99fJ99ikf/rs50i3ltn+qG508mBhg4uX1oSemNgiuGx++hQM/eW72PZlNyeDhHRJMEmFOekxXQ5NKGvAAAgAElEQVQxQDSwu8AmkJoUo0WIUWJynuHpPdaBt4V3cFq6upk9UUsdiQiFWSnUtTroMozBvtXEoLUw/7Mvdg0s0xhcMCmbvIxkynfXc/GiSSM9rCEj4COOUmqriJwOlIvIqUqpd4z364EapdQbwzbKwdK4H8aXRU3qYkhJz9Ph1v/co6syBxjaTrE3aOHqUp/6n8xCmHUe4/a9CZsP6e/BlgCS4DO36bnYdIi5+VmYeZbXuPPHe3+Ad34DJ3xFFyaAzm/LmaKNwROv9W5rb8WVGN6TqcUgOeN2WPGtkCH55tz5cME9sOYGmLpcLyyYA2IjzV4DM8+FtMhalg0BRwE3gFJqdP8aB2BLpa5oXBxGviDoXDGbwN2v7sajFHdeOj8sj2IkTM5LH/3GoFuRkZI4ZN9hrFCUlUJ6csKARKFbu7rJSvWWFRRlpVLTYu/JO/TnGSwryODJjyrpcLhiMvRe02pnYWkONptw2ox89o/y9nlB/wJKqT0iMk0p5TDeHxWRxUqp2DXn/XFsf+9uCbHOSdfratAP/gLn3DmgXbNaDWmavh0tPvMw/wm3cXe3HX45Czb9K7AxuO0JeOW/Yc5FcO5dXkPb9A5uexxcTp2jBuBotcLEw0Visvd7D8WSz8G0FZBt5CUmpeq8w4bdcHzshIiVUkPW2y5e2Hy4GRGYXxKePJOW+UjncGMnN55xHDOCiFQPlpLcdF6pGmL5qhHG7oKMELIsowERGVAI1+1RtDlcvbqaFGWlsKumrSdMnJbU/3vzTWOYVxxbkmNKKWpa7Zxj5D/+7NL5pCWNumBDL0I+YZuGoM/7XoagiOSJSOxG/9wu3VUh1otHfBk3AeZ9CjY9pCt1B0BW605dQDBhQeTnT0qFBVfCzuf8tzDb/5YWL55yKlx6f/9Q8syzdUHJQR+JGStnMHbJndpbf3PiAty2ZK1RGAOIyCMi8pCI/EVEfi0i3xaRhca6z/ubRnrMQ8HmI00cV5DJuNTwhR3mTsxiekEG3zh9aGW1SvPSaOxw0u4IvwI13tCewdFtEJhMy88IW/alzaf7iElRVqoOE3cHzhk0WybGYt5gc2c3TpeHoixtDKYnj36PcKBq4rkBpokicomIfNVn878Ca4ZnuCHY/yaFtW9Dy1HvsuZDOicq1otH+nLS9VqOZcsjA9otu2UXTFoCCYNUAlr0WXA7tAfQl6rN8Og1WhPxqn9rw7EvZafrHM2dz+n3Xc3QchRncu7gxmQxPJzxfbYu+CGkDJ0naYBciS5qU0Ax8Fm0rMxi4EHgW2hd1BuBB4hn5YMAKKV6ikcGwq8+vZBnvnEqqUPs1TDzzEZzqNjuis/ikUgoy8+gsqkzrA4hvn2JTYqyUml3uKhv074kv8Zgvr5mDsSg1mC1oTE4ISt01f5oIZBncDtaz6vv/KfG+ntE5CoRuQG4GK/cw8iy8e/M3fkr+PVc+ONy6GjwVrXGk2cQoGQZTFoG6+/z3z7Ml8YD4HJAdxeZ7Qd0Ve9gmbgQiub37mvcWAH/uhzScuGaJwPnkyWlas3EXS/qgpStj4HbQW3RysGPy2LoyZ1KS07MtW38vlLqWqXUp5VSS9ByV6eiDcSLlFInKKVOQFcax1TVSzQ43NhJU2d3yM4jfUlPThyQJzFSvFqD0TUG731rHw++cyCqx4wUu0vFZG7bUDCQDiGtXdob7OsZNI0o0+vnr4AkPTmRCVmpHIhBrcFaQ2NwQnbsBj2jTbAwcamRqL0BXVL0XwBKqWeAG9AewV8D342ZdnSX/40Pl/5Kt92q3QYb7vdqDMabZxC0d/DYXtjzsv/17XXwzNfht4vgwQug4m1syqX70Q4WEVj8WS2A/c5vdH/af16qvazXPAVZE4PvP+dC6KjTgsYbH4SJi2gfF+MdYCximb7NQW2An76HoxNTbHqgnsHhojQv+lqDTpeHP7y1jwfePRi1Yw4Guzs+NQYjYSAdQkzPoK8xWJiljaiDhjEYqAVerMrL1PQYg9EVaI9lghmDymduTmUichfwY7Ti/01AoYjERnWfLYH2cdPh1Jth5jnwwf26L29Klu79G2/MvVgn87/y39Dto6rh7tbyM79bqr1uC67SRttjRqpUaRBJkYGw8CqYtBRe+wH8abnub3z141AwM/S+M1aDLQne+LH+Gyz9YnTGZDFWuVNEvurTcu5HwPPEc5vMAWC2lZteMIDWhMNIbnoSmSmJPS3IosGHBxvpcLo5dKwzJgR/7S5FRpy1oosUs7gjHEPNnzFo5tqZHUYCtfCbVhCjxmCLHREoHGd5BsF7k/W92c4CUoGvAzOBPwGfJhbDMqfcAJ3HdM5dXpzIyvQlMRku+JXuq7z2F3pZRbk2zF65TRt9X38PLr0PPvMwiNCZVhw9wzctF659E76xQcuVfPbx/lXKgUjNhrKVcOgd3dt2/uXRGZPFWCUJnRN4SER+BzyrlBoznsHqFjvjM5KHPPcvUkSEktzoag2W7/HWKm4+4qeQLcrsq2vn249tCZgnZ0rLjAWy05LIz0wOyzPYajdzBr3fTVEYYWLQbemaO7tp6nDy1q46bnl0c0/7xOHA7VH838u7eOzDI72W17baGZ+RQlJCbPi5hoNAV7bpCYTe4ZmXlFI3+W4oIk8AVwDPRX94g2Dqcp33Vr0l/vIFfZl2mu4x/M5vdc/Yva/o6s+rHtbVnqaRO2M1XFfOjg3riULGYG8KZulpoMy5EPa9rg3B2ClGsIg/FHCrUqpKRM5HF43MMltmAp8XkUaf7b8ADEyTKcapaeliYk5sJ7NPzkuPqpenfHcdiyfnsLWyhc2Hm1kSplpSpDz47gGe/KiSK5eVcFLZ+H7rx1IBCfQO4VY2dXKgoYPlx+X3q6r15xnMTEkkMyWRhnbt0U0P8BBjeiA/rm7le09tpbbVwXfOmRX1/tn+cHsUtz6+hac2HSUl0cby4/J7uq9Ut9jHVL4gBPYMCrBJRKqApcAO4DfAVSJS5TsBX0YncscWInCKIbwcj/mCvpz1E0jJhANrtRD119f77ztbOIeOzKkjMkS/zL1YCxefcmPobS0sAmNWEqOUegH4JLAEWASsNd5fYUxvA6tHZphDR3WLnQlZsZ2/VJqXzpGmzqh4dqqau9hT2855x09kZtE4Nh0ZWiew26N4eXstgN9zudweuj1jJ2cQDHkZwxi8+ZHNfO6vG/j0fe+ztbL399PS1U1SgvTT4Ssy8gaTE2wkBvCwTSvQxuAda3ZQ26orj3fVDExOzaS+zcG7+xrC2tbtUXznia08tekoXz51Ggotzm5S2xr7/2/RJtCVHexmmgr07WB9xN+GI868S+DohzElnhsRGflw3duQmBIfLfVM0nLh6oFJ41hY9EUplQYgIn8DbjG6I30GOF8ptWpEBzdM1LTaOWFq3kgPIyiluWnYuz3UtzsoHDc4L2b5bh0iXjWrgAPHOnhuSxWesqFzDW481ERDuzZGNh3uH5LucOjQ8dgyBjNpaK/krV11bDzUxHnzJ7C+opGLfv8Oly6ZxK1n62iR2X2kr8ewKCuV/fUdAUPEoKvQbQJ769o5bWYBa/fUs6emjdNnFQ54vPe+tY+H3j/E9h+dHTSdwuNRfO/JrTz5USXfXj2TGz85g+REG/et3c+XT53G8ZOyqWm1s2zq2JJC82uuG63m3kOHhd8ArldKvWG8XgO8Z7zuBr6jlNozbCMeCAlJcO7/QeGckR7J4MmdEl+GoIVF9PkCYPY03ArcJiKLRnA8w0KX001zZzcTsmM7TDzX6CLxTpjemWCU765jUk4axxVmsqg0hza7i5qOocsle3FbNcmJNs6aW8RHh5v7eTc7jLZqmWNEdBq8IdwfrNlOXkYyv7xiEeW3ruL6ldN5fks1p99dztN7ndS22nuFiE28gs2Bv7PkRBuleekkJ9r42aeOZ0JWKrsj9AxuO9qCy6PYVxe4bZzHo/jvp7bx+MZKbjlTG4IAXz99OrnpyfxwzQ7q2uz6/20MaQxC6AIS8694iYjMEJFZxvIZIjITOAYsFpE4d71ZWFjEKiLSLSJO9L3nkPH6CDpK8YGIOH2mThF5fEQHHGVMmYuJMW4MnjA1l6nj03l4/eACRd1uD+/sa2DlrAJEhCWGtuL+5tACyJHg8She2VHDypkFrJiRT32bg6qW3sGvDofZY3fseAanGyHcI41dfOGUqaQlJzAuNYnvnTubN769kk/OKeLZ/d28vrOul+C0iWkMBvMMAnxj1XH87FPzKclNZ9aEcb3CxB8dbuKP5fv5zhNb+Ot/AutNuj2Kj6taAdhb59+Y9HgUtz29jUc/PMJNn5zBzWfO6FmXlZrE7RfMYfORZs65Z12v8Y8V+l3ZIlKMzg9c12fVG+ibsQJewFtY4gDGfN9QCwuLIWMZ+t6zEa1cUGcsnwY8hRa+P2wsWwr8brgHOJRUt2i5llj3DIoInzlxMne+tIu9tW0R90KuqO+gw+nmRCMsXpafybjURCpaQojvR8jmymaqW+x855xZzCjUY950uIlJOd6cMbPN3lgqIJk8Ph0RSE1M4POnTOm1rjQvnXuvXsKitDd4vS7db0jVzBkM5hkEuPIEr/kwa8I43qs4pnM03Yqr738fe7eHrNREHvuwksyUBD59wuR+x6iob+9pfbentr9n0ONR/M8z23nkgyPceMZx3OJjCJp8anEJ0/Iz+dajm2nscPZoZ44V/F3ZHrTH7ycAIvJloFIpNc147wZmKqVGb98hCwuLmEEptQVAdFLSDqVUlbFqi4jsAhYopZ4ztilFd0waNdS0mJ7B2E9ov2xpCXe/upuHNxzhBxfOjegYu2u1Z2fWBG2Y2WzCwpIcdlYf49UdNXR1u7F3u7F3e+jqdtPl1O97Xrs8uNweblk9k5lhGKTPbjpKUoJwxuwi0pMTSE2yselwMxcsKO7ZZizmDKYkJrBiRgFLJueQm+E/X3NGbgLXfuoUv+vMMGt6Uvjf2ayicThdHg4e6+BAQyf2bg8PfOkETptRwBcf2MDtz+5gXnE2x0/K7rXf9qoWANKSEtjbxxhUSnH7s9t5eMNhvnH6dL61embAPsOLSnN44aYVrD9wjJOmxXaObrTp91dSStUA14vIrcAdaC3Ba3w2qUIbjBYWFhbDyTSgus+yzwKHfN6/oJR6PpyDiUgScBf/v73zDrOjrBr47+xueiWdJGDYQEgglEC+kEjJIiAIiAhKlaKgKGJBRfxEIEr5RKkqIKEqKFY0SpUggRBKQiAEEtIISdgUUjZlN313z/fHeWfv7Owtu5vd7E32/J5nnnunvHPOvHPmzJm3mn/rDEwBLk/XBlpEBgDXAacCe2DT4V2jqs809CIayq40T2qvzu048cB+/P2tUn540v6NGhdx3opyCguE4lBNCTBqnx68smA1X3t0eto07YoK6NC2kA5tCmnfppCPyjbRu0s7fva57NMqvvrBah59fTFnHjawpt3bwQO61+lEEpUMdmpFbQYBfv+Vxk9g0Kee1cRxog+AOSvKeWX+ajq3K+LIwb0oLBDuOmcEp/5qMl9/bDpPfusoundMBajvlm6gfZsCjhnSq1Y1s6py3YRZ/OGNJXyjZDA/+PT+GQPBiA5tCylpRAeWXZ2MIbuqlgPfF5EHgQ9i271K2HGcnY6qLk6z7e3EekN6GfwUOA84A6tm/g3wtIgcoKrJKS9OA7pi1dSlwFXAE+HYZp08d8X6LXTv2KZBL9WW5LxRe/PkzOU8+94KTh8xoMHp535czj69OtGuKHW9l40tpuOGJYweNZL2bQpjgV8B7YsKKSio/YK/9HfTeHHuSlQ148t/ZfkWvv34DPbp1Ylxpx1Ys33E3t15+NVFbK2sqtFhYyusJt5R6ltNHGffPp0pLBDmLC/nhTkrOWZIL9oWWdeGHp3acvf5h3HWfa9x5Z9n8OBFqRF131u2nmF7dmVov678Z/bHbN5mJbk3PvU+j76+mMvGFvPDE3MHgq2ZnMNrq+psVd26M5RxHMfZGYhIIXAZ8H+qOjkEmpcCxcBJyeNV9V5VPVdVZ6jqamxKzvbAJ5tSr8rqurGsjTGY/6WCEaOLezKgewcmzFjaqPRzV5TXlBBFtCsqZN89Chk+oBv79unMgO4d6NGpLR3bFtUJBAFK9u/DR2Wba8bJS1JVrXz78bep2Lqde84/vFb174i9u7Otspr3l6dKmDZta30dSHaUaHihhnzEtG9TyKCeHfnnjKWsKt/KcUP71to/Yu89uO7UA3hx7irufnEBYO0BZy/bwEEDujGkbxdU4YNVFZRtqebBVz7k3FF78aOThnogmIOMwaCIvCwira+s1HGc1kAx0APrlAKAqn4MLALqUzcWNSgrbSqFZi/bwJUvbuLmp9+vNZPHig2ba2ZG2BUoKBBOPWRPJs9fzdoGzim8tVJZUraJ/RvZ+SSiZP/eALw4Z2Xa/Xc8P4/XF5Zx4+kH1Qk8R+xtnSHeXJSa1KYitBn0ksH607aogMG9OzFwj4Z1xBjaryulazdTIHDs0LohyJdGf4LPjxjA7RPn8d7qKhat2UjF1kqG9+/GkL42d/f8leW8ucLu2aVHF3sgWA+yWfZR2JcvIrIky3GVwKOqen1TKuY4jtOM9A6/ZYntZUB9PoL/Fxvr8OV0O0Xka8DXAPr27cukSZNynrC0vJrirsoDkxcy/uWFXDK8LUcPbMOSVRvpVbCpXufYUSoqKppETv/tVVRWK3c98RIle9UddiQTC1ZtBITtqxczaVLtksWG6ta/s/CP1+eyb1Xt19fMVZX8ZvpWjhlYRK/yBUyatKBO2r4dhSenzqtJO2veNgTl9Skvt1hgke76m+p+NZZc8q86RCkqWMqkScsyHpOk7Wb7gBjcrYCZ015Ne8xJPZVpnYR7Z2xmyQY7ZsvyeSwuL6BQYOLU2by/eht7dSnko1lv7vRZMSoqMo91mK/U9zNnIPB5bFqoPsCK2L6hwE2AB4OO4+zq5HzTi8h3sHaGozO1UVTV8cB4gJEjR2pJSUm9hA/sMokDDhvNhQ9NZdq6An54zhg2PPsshw8tpqSk7nAYTc2kSZOor67ZUFV+N+8l5m1pz7iS0fVON/mx54FtnHHcmJpBjxur26mb3ueRKYv4nzFH1VQDL1u3mSt/NZmh/bow/rIjM3Zw+VTZuzw5cxlHHzOWwgLhvnmv079zGccee2y95Tc16a6/qe5XY2kO+Vt6reAfC6ZzxughlJRknkp2v0MqOPnOl/jr/O20LSrg3FOOpU1hAcUzXmLR1gIWlm/nqhP3o6Rk3ybVrz60ZIDeWHK1GfyXiByBjSn4PHAs8A1VfS5agD9jPescx3F2FVaF3+T4EXuQGsewDiLyVeBm4HOq2ix+r0/X9nx+xABmlq5n+mLr1ZrvYwwmERFOPaQ/ry1cw8oNydlLM1NaUU27ogL2boIx3kqG9GZbVTWvfrAGsMGsr/jjW2yrrOae8w/L2tN5dHEPyrdUMmvZetZv2s7URWWM6ONVxDuDI/ftyZmHDeTMw7N3Piru3ZlLD2qHKgzr14U2Yf7jIX27MCsMQH3yQXs2u767C7mCwQ9IVYMcA1wCfElElkUL8F9VHdmcSjqO4zQxC7Eq4RrfFdpIDwKmpksQxly9AzhNVV9qTuU+M9xeYg9PWQTsGmMMJvnswXuialO9ZaO6WtkSBgxeWq7s19d6lO4oIwf1oFPbQh58ZSELV1Xwi2fn8NaSdfz8zIMp7t05a9oxxT0BeO2DNUyat5KqauXQPrtGb+5dnS7t23DbWYfUa37rkf2KuPnzB/GtT6VKzfftY/f2E10L6pQuO5lJGwxKqlHElcBhWNXJduAyVV2Ajcn1WWy4BZ8w13GcXQpVrQLuA64WkaNFZG/gfixIfFZEviciy0WkGEBELsaGnjkLmCIi7cOSfjTeHWTvnh05sH9XJr7/MbDrlQwC7Ne3C0P7deEvb5bWmes3YvayDXzmrskce+skStduorSiul4DRdeHtkUFXHnCEN5aso7jbn+J+yd/yIVjPsFnD+mfM22fru0Z3LsTry9cw8T3V9Krc1uKu+UcfMNpAc47Ym+OPyDV6ziyn//p58F7Q6hj3WFw1XcI082p6qyw6zVV/Vv4X6mq01X1zYYIE5E2InKHiKwSkc0iMjHMcew4jrOzuR54HPgHMBfoBJwcxhgsADqQGmD/+rD+FLA5tvynuZQ7+SArWYNdMxgE+PKRg5i9fAOT56+utb2qWrl30gd87u5XKNu0jYqtlXzpgTdYt1V3uCdxnEuPLmbK1Z/i8pLBnDFiANecMqzeaUcX92Tqh2VMmruSTw3tQ4H3SN0lOGrfXpwxYgBHD6h/xyUnfcngSuDpaCVWSthPRAbFNvcWkb40jPggr0MxZ/p0c31dO47jZEJVt6vqlaraS1U7qOrx0ewjqnqrqnZX1UVhfR9VlTRLSXPpd9Jwq3Tp0r5olx3S5PQRA+jbtR33TqqZt4CPyjZxzvjXuOXZORw/rC/PffcY7r9wJMvWWdvC5FAvO0rvLu246sSh3H72obUGss7FmME92bitivItlRw/rKGvOqel6NaxDbeffSjd2nnw3hDqBIPBQf4Iqxr+AvAuVkp4HDBVREYC3bAexdkbg8Ro6CCvjuM4rZnBvTuzf98u9N8F2wtGtCsq5KtHF/PawjW8tWQtf5n2ESfd+TJzlpdz+1mHcM/5h9GjU1tGF/fkrnMOZUBnm4c4Hxgd2g22KyrgqP16tbA2jtO85PrcvBorzfs18AcsCPw70BPYEI6p7/RPaQd5FZFF2CCv/6q31hn46b9n8erszdw797UdPVWjWbcuf+Xns24tIT/f9Mkn+c2h2wH9u3L9Zw/MfaBTw21nHcLWyqqWVmOHOGfU3vz6vwu4+KGpbNhSyRH79OC2sw6pMxjxZw7akw5r5rJHp/yoKOrVuR0HD+zGwD06+Mwjzm5PLgs/WlXnichvsGrdp7ESw35ATZ9tEUFVZ+c4V4MGeW3UoK2lW6mqqmLdunU5j20u8ll+PuvWEvLzTZ98kt8cupVWb2DSpFW5D3RqGD6gW0ursMN0blfE144p5q6J87nm5GFcctQ+aaeQy0ceu/QI2hR4xxFn9ydXMLg59j/eWeSWxHEKNLbrTlqv0JhBW0tKds9BOJtKfj7r1hLy802ffJLf0ro5uxeXlwzmy0cO2uVK2Lq2904ITusg25O5FKgCUNWm+DRq1CCvjuM4zq6NiOxygaDjtCYyBnmqupeq1n9Cwdw0eJBXx3Ecx3Ecp3lJ+6kmIn8CKoEtQDlQCkxU1XdE5MJ0aVT199kEqWqViESDvL4JLMY6piwEnm38JTiO4ziO4ziNJVO5/VnAI1hbwP7AWOBmERkdts/EZiQBm6FkJpA1GAxEA7f+AxvgdQqpQV4dx3Ecx3GcnUy2Rhw/iVcTi8g7wJFYgHiaqi4J26uBU+ojTFW3Y1PcXdlojR3HcRzHcZwmI1vHkOT4gQVAy42D4TiO4ziO4zQ5km4C8VDa93vgNeCfYXDoLwATsU4ggxIlgwObuLNJUp9VWBvD+tALWJ3zqOYjn+Xns24tIT/f9Mkn+S2t2/6q2rTzkrUQ7r+aTH4+69ZS8vNRp3yR35K69QI6qWrvnEfmCdmqidsA3wLuEpH7ge+p6nZpgcm6G5KhIvKmqo7MfWTzkM/y81m3lpCfb/rkk/x80K2lZDc17r+aRn4+69ZS8vNRp3yR35K6BdmDWkJ2Y8kUDCpwlaouE5FTsE4j+4vIiWH/hSISn0nkIuD/mk9Nx3Ecx3EcpznIFAxuI7QZVNWnROQ44L/AocDLwHGxY18CTsCDQcdxHMdxnF2OtMGgqnYAEJGHgCtVdaaInAucoqolO1G/xjDe5Tdq384g3+Tnmz75JD+fddudaenrzmf5+axbS8nPR53yRX5L6tbS+dJg0nYgqdkpUoV1DlkuIn2BD4FPquqMnaWg4ziO4ziO03xk6k28HasmLsJmIokowuYrjieqBJ5S1S82o56O4ziO4zhOM5CpzeBIQIDp2IDSK8P2fYAngM8BS8K2w7Fp5RzHcRzHcZxdDVXNuADVQP/EttnANbH1a4H1QAU2ps+fgT1j+y8BPgC2Au9i089F+9oDPwHmYvMgLwS+n5B3Ljbe4WZgBVYX3yW2/0TghYT8vYA7gFVYZ5hN4fdd4GRsJpV/YgHttqDb9nrIX4MFxluAj4Cr08h/AngGKz1dF/JQY+vnx+Svie1TYHkkP5Y3y8I5qoENwL+wuaIfSZM3GxPnU6wktxIbHzLKmz+Ha47rVprm3vxfyJdqYBHwItZUYAvwPnB3TP7aoF91kLkO+GMib17DbEVjx8XvzZ2kbOW9oGck/8OQr3FbeSxx/TPDtW4nZitAu8S1RkslMBl4mPS2ci6wNE06JWYrwADgvpDHVbF7FbeVB4A5sf0V1M9WhmSQv7Ns5eE0x1UTbAX7aPxuTHakWzZbWRXyJp4XNwGFCfsbH863JqSbCAxJPJ8CXAG8Fa6pAjg6m18L6YYDs4B3qOs/pqW55u24/3L/lV/+S4GPgT9htrIZez8vCTrMDnqne35r7JWU/1oa0lWEa9oe9Ckls/96OpzziXBsJGMDcH64F+l8WDW5bWU88GnMVl+i7nO0PM15q2hd/mtSmn2vprnmc3KeM4fATxCqkmPbRgA9wv+iYAzfDhk/FJgB/Cfm6CqxgLAfMC4Y27Cw/3Csd/LxQE/gi+H488L+bsEILgzpRwbDGJ9D/kLsIfl+ON+McGNvCPJvAr4DnB4y7tZwU2/MIv9AoDwsjwJnYo5gZkz+OaSctwYjqgo3rRQz4uog/96wbwFwD6ngpBI4L+TNFOxldTnwVVIOaBXwh0TeHBHO8TRwS7j+N7D5oJ/HBr19J+TNBmx+6ErMia0KabfF7s0RmEOcCbyCOdZq4Lpwr64KaW7A5q6OnN6bwA/CuTdhDk6dJmsAABWCSURBVHAv4Jth/7ZwDbeQchofhutRzCn0A+4P63OD/KvD+s1B/kVh/R5gb8wZVWMP1K3EbAUoDse+hD2Qq4CLw33ZGHS9itq2clM435yQ9mgscPwMsCcxWwW+AfwtXPvlwIMhzUbMVr4X1pcAP8LsZHUsj7LZysWkXojzgrxbSTnq5raVspj8mdhL9WbCc4zZ8aKg99nAvsAvyWwre4drU6zm4XTsRbwS+GmwvWFB1oJw3BcxX/TvsK1tzB/di9nI57B51HsC7XP4ta+Ga34Zc9RJ/7EZuAw4NVz3ZeGej8P9l/uvlvNfw7Fgc0O4V58iFRQfC1wQ/n+M1eItJhXgdAHGkPJhcXt9CHgcOCrcs+dDmqewAPE1MvuvKO+3h3y5KVxvZZA9DPhhOGY+Zhu3kdt/Rfm/EXgbs73kc1SNBcKRrdxA6/Nf6YLBJZjPah9bCnYoGGzMgj00W7GIdwI2g0l8/yzg9izppwH3Z9n/S2B+lv1XhBvw3Ug+0Dfc3NMyycccxzWZ5GPzKa/GHtT5YdvdwFuxY64lFQhoGvm/Db+3J/MmyP84GFo6+V2xF9dszBk8kti/d5D5YTjuu7F9kfz7suRNRZB/e0hzX7iWcZHBJfMGcwTXAHcFAyyL5c1T2HBEG4GOWFOCO4P8siD/HVLO7I1wXXH5azFHlEn+NMzp7oV9Zf8KcyDj4rYCHIM5n7bxvInZSiQ/nh/PBvnzgc0NsVVSjv+pkP5nIX/itvJ4uN5ctvJMyMN0z9HOsJXHIvnUfY4exb6cB+fKm5itfD6cY23MVq4K65GtXIIFO2uDDoMSup0W1g/NJD+LfygCfoe9WMeRcKYxmxiL+y/3X/ntvwpJlaaeFnR6hpT/ejDotL0h9krKf10QdFhDZv8VDUP3bmQPsXuiwVbeAjbW13+F/dND/n2IlSIn7eW34fxrc9hKa/NfBcFWxtb3nNGSbW7ixtIfWKam2UjspsZ5AxiVLqHY9CZ7Yl+h2c6fbf8B4Xd6JF9VP8ai/1FJ+SLSUUSuJ/UVnUn+SOzhj+9/AzhYRNoBqGr05R5PE5c/HDOYUdG+hPxpwB4Z5JdjJRaV4RxJ+sV+ewCXiMglIiIx+Ycn8yacdx3QKcgfJSKXY4Z6eSyfat0bEemMOfjScK5lpKprorzZH3s4ilX1W5gzI+TRyCB3WdBtGOb44vInYFUY6eTXrKvqR6r6JeyrKp7/ka30wz5O5oa8uUhETidlKyuC/OhelQEHBfm9gLYi8oGILBaRZ0Xk6By2emT4bRv2d8NeVDW2gpUsxKdby2QrQ4EOwDFx+SF/d4atbIjkA1Mx53VmkD8W+2r/gogsEJF3ReQ6ESkiu62UYU4xbivdSdnKg1gJUfe40onnGOCMLPLToqqVqnqRqr6X4ZDB4fdhrGRwpIj8UETaxHR1/+X+Kx/8V3HQr4rUPXmVlP+6PORPUdJ/pdMpRuS/lgQdNpHZf60P6/3C/Yie0/VYoDgKe6baxHUIeZbWVoJefbHSv0XJ/YFB4bc78GMReVVELsFK6BbRev1XLyxgfFhElorItIT/ykiTBoMi0huLiqMOJb2xjItTBvTJcIoLMAN5KMP598ci87QdVoL8s2Ny4vIjuWVAHxH5vIiswb4ozwVOwh6uTPJ7Y8Ydl1+GfZ31TCM/ShPJ3wAchlVD9AnL1Qn5Bdg0gOnkX4A5gv2w6psk6zFHck9Yn4B9+f8gJn94Ii8i+R2wL8o5WHH2/wJnqGrcMSXvzfewL8a/h+tcEfR7JezfTMrB90nkTXVIMwLLyzLMma+Ny8dKMjplkF9rPWYbSxPrvw46jsWqKwg6PhHOEeVHJ6BMRMZiD/6LQX41VuR/QjjfgrBvHJlt5XDMCR8Z5E/CqukKgZ4i0h/74o6TyVYqMWd/Z0L+UHaOrdyfkF8Rzt8uXP8wYCBWXfIdrPrrr2S3lbXUtpX4iAWRb8g0hVvcfxyYQf5PMqTNSrDRc7HSjFOx634Bs5s708hP4v7L/dfO9F8Xh/VVIT96h2voCrwYrj9qP1jLf4nI6DQ6RRyO3Y8VIc0fyey/omkju4d8iOyoc7j+Ptj9+XVCh89QP1up86yEvDkGq2IFizn+QMpeWqX/AlDVldhzeirWdOBX1PZfWRM3yYJFxVOwiLggbNsGfDNx3G3AvDTpx2A36rwM5++NfRndF9t2JGZw0TKXVOPJA+Pysa+W8ZF8zED3Chl3Mxaxb8TaMEyInfOtkP5F7AUflx+1hShLI18j+SFvKrAHM5K/DSt2jsvfjjVerSU/ljfLsOLvR8ISXf8azCAnAp8Msr+EObSqcMwW7OsunjeR/OWYgf8l6DA5Jn950KEi5M1bpKo3Xwn58EHIg6gNVtTWI8qHdYm8iRrrvoe9QKYHPV8J8o+K2cqaNPLXhfO8mbQNLPD6BdZuZHPsOq6LpVsb7mV0HbOC/GsxZzyHlK2sIVYUH+RHDaXfTHOvvhOuYSO1beU/sfyI7olSf1v5Zuxcc8M5dqatxJ+j1Vh130ZSth/Jvznc2/MwRx7l8bqQ/vdBVtxWokb5ka2cE9Otppol/hyH/88DryX8xKXAwvD/1zHdVqfxKeNIVeFl9F9YteR27AXi/sv9Vz74r+nhnJH/Gh/yZB3Bf4VzvwpsSuO/tobfN0nvv14gZq9YQBj3X9EQcz8i1WYwspUp2DM9k/T+a0zIv01hPW4vc4nZStj/CPZcZrOV1aTspdX5rxyxWY3/ynpcEwWC7cOFvQF0jm1fClybOPYhwkMY2xYVuV+V4fw9sSqOCUBRbHs7LKoeHG7ONOAQUg3+a+RjjUlvzCJ/OzAj9nAODEu/IH8t9gUWl/+VYJhD08jXIP+nIW+2YEXrD2FOI65bJH9N2BeXf2IwtmXR9ZNypu2wL6DZwHNYsf1+QfansC/UbUG3LaR6N6XLm8ghapalBOtNuBmrgugd8mYj5ryKYvfrZ6Qa705J5M3WoNcNMfkbSFXVZJMf5ceNMfk1thHybwXWWPcTsXzsjjWQ1nD88qBz5Nw3YA6wPvKjnpu9E/fq+yEvyslsK2PC/VhM7YCwIbayLeRBS9nKrLBtFfYVHZdfAWwLx55FylYGhnv1ccijuK2cQOolMwR7oUS6JZ3pQuDG8P9x4LHEs3wioX0S9nUf6TYwkzMlh//CSjEUq/Jz/+X+q6X919lB362k/NeN1G6DnEl+P6w5QSVm/+n81yTMh9XYK9Z5I+6/SrEq2fPDuVeRspU3sLZ+M0hvK+swW1mbsNeTY/kYl/0IFoRls5WB2LNaTSvzX/WIz2r8V7MGg5gjfQ6rj++e2DcBmJDY9h6xBtDYl10Z8OMM5++B1av/m1gvnEzysaLsNViD6Qlh6UOq4Wgm+QuAf2WR/x728o4709+EfUn50ZfCv7EH5q2k/Jhucfk1DZBjuq3FHo6a6yflTOvkTeL678aMO5J/epa8mUGqp+GhYTkHe7BKw/rp2INwQSJvFgIzE/n2ZNB7VSxvOpLqcfhUQv4baeR/RGpog0OxkoQaW0lef1gvx74u09lKR8yJL8a+NJ8i1f7jDazR9bFh/TvhnryDfT3H5S8H7k6c+yshr+dTP1t5B2sE3RhbWR7uY0vZyoekXvwLwnki3f6JOduTSG8r0zCn2TOm+w+wF9szsW2F1G2AXfMch/UfR/Jj6b4BzKqn7xqHvdiy+i+smqgCa7fl/sv9V0v5ryhv5oR8fY6U/zoN819TSPmvQ8P+FeF/53BPKqlbIhX5r9ODvluBDjn8171YIKbBLlaGffsGHRaT3lZ+jAVZM3LZSsxe/kh2W2lPqjS1Nfmv+gSDNf4r63H1EZpFSDvspfoW1uAz3pW5kNTQMl/Bvkiuo/bQDIeGmzIukbZ92L8HVqz6LNYOolZX6Szyb8EegGhohreDAfw0yL8f+0I5htRDVY19/WSSX4x90fwWa7x6ZjCWmWnkR0Xq74ffd7FqlBuD/AewXoNRY+oXg/zt2Ndne1IP8jKsirE31ki7G+Zw/pjIm/Oxou39sDHK1mFGWxqO+QD7mrolGMYdQf574R5E8pP3ZhL2wjwl6Bp1We8Xrvs5bBiHbVg7kgNIfaFVYl9yfYOOz2IvmiqsvU982IzHQl5dE84dDVcxNcgfFfS5MSH/P+Hc0XoZNoxE3Fa+jjnLFzCHVR50OB8L3uJDyyRt5dWg47GYrUzBXjAHxc5/MVZdMD/oMyzk/3hStrIp5PVs7EWyDPvyz2UrtwXdqkL+Tw33aRs7x1bKsZdyZCvrYvKPCHL/En7vC3l3L5ltpVe4vmeD/hdgDnwrMDzhX+4OefJFrLfpBGJDM2DPZ1mQ1x9r67QQuKKe/usGUtV40fN7EvA17IX2hZAv5Zhtu/9y/9VS/uvkcN8+JFVNG/dfx2HVpdVY6dVemL1Wh/vYB3sfR5144v7rq5h/OivoMIna/utsMvuvT5NqUqDYs/CfcK1bsWraB4K+FZj9TQnHnkZuW4nscTW1n6PIXn4TrncBqfbdrcV/jaNub+LjsHirGAtgT8M+aK7Peb4dDAbHkrlI+uJwzCXhArdSd9DWhzOlD/svynL+khzynwsGlG7Q1r9gTqYyU/p6yF9Jqnt7Q5epQX5VlmMy5k1YZmbZt5G6A8LGlxUJ2dVYe6B63Zt6LFWkXiT1WaLxwu4kZSu5qnwastxCqko43RK1MUpnKxNJOdBM6T/MIX8lqTYsDV3+ijm3lrKVedS1lQWkBm09nNS4W41ZqrGSkIPT+Jd9wzFlZB609fBw/6JBdH9EPcbUqoeNR+3GPgq24f4rtbj/qr3sDP/1PvYMZDom6q09G7PZyF7fDts350ifa2ns9VyMVfdnk72jtrIty/7d2X+No24weATwOhb0Rh97VxArfcy0pJ2b2HEcx3Ecx2kdNMc4g47jOI7jOM4uggeDjuM4juM4rRgPBh3HcRzHcVoxHgw6juM4juO0YjwYdLIiIm1FZGA9j91TRAY0t05p5HYRkbNj82ZG2/uIyKd3tj6O4ziOsyvhweBuiogcKiKVsfUtIqIZlr9kOdXPgGdEpD62cj02F2Ik8/gsMocnE4vIN0Xk++F/JxEZmGHpkUg6BhtvqjKx/VvATdkUFpErsugYXy5Ok3aFiBwfu9YV4X9JmvR/y5l7juM4jtMCFLW0As5OYzA2g8IvsKDpx2H7Ddjgt4hIKTblVjqqRCS5bYqqHhXSRgPHXisinbHxlcAGvDwske6j5IlE5OCg2xfDpi+TZpLywN+xAYERka+H/xuAm4KOPwnXemk4Zy5ej8lNx1MJXQUbEFWATiLSHZuQXsJ/wca2GhSSfA8bdNRxHMdx8g4vGdzNEJFBIqLYYKOFoVTqEVVdqqql2Kjpr6lqaViPJvYGGA3sg82gsRqb+WKf2PIPbBT34rD+hZjo67Hpju7ABkBdH7ZXR7JiMpM6D8FGdH9YVZ8GUNXfqKpkWOJyv4QNyvsMNur81dhHzjewkeNvT5TQzU+TbVuTOib03Z44PprrtQ82fdHa8Nsn/O8ZriFKvyGNTMdxHMfJCzwY3P34CJuK6EQs0NsLm58RETkFm+7pn7Hju2LBWxS8LFLVOdjo5l8GlqjqIqzk61Tga6r6YTguqhY9EPg28NVYwBaVOvcQkdfjS1xZEWmPTZw+GfiOiIytZ5V0nHtU9QpsPk6wKZBuwQLDvWLL97ER43cIVV2tqoLNKnJC+H8C8HH4vxpARIaKyFBsCiPHcRzHyUu8mng3Q1WrgFIR6RXWS6GmGvZ3wAOqujyWZD9s8m3CcckpaZLVw6+G9aWqGnUs+QCbo/HnIvLbsG0CNufiWiyIjLMqpu8WETkLm6/yuJBumIhU5bjUVaq6NcO+U7D5NW9V1ZrgT0Si+U6bkudj+fNxbHshELUT7IPNT+o4juM4eYcHg7s5ItIPm5T8T9ik6leKyBexat1qrEp4YiLZKGwuzPOA/2JzN44B2mBBzfFYhw3AArpw/j+FdofHq+qc0LmiPXWDwVqo6mQR2Qub7P2nwFLqVs0mORYL+NLxC6zN4igR6aKqz4fthdTtZALQLkeP6TZZ9p2gqhPDtT4W216lqsMBRGQcUKfDjOM4juPkAx4M7r4MwoKfRcDNwKeBt1W1WkQmYkFad+BVVX07kXYz8HVs0uxRqloROkaMBx4AXmyAHm2BEuAkbNLwhVgJ5drogDAczQvASuAXahNm1yqOFJF9gflAb1VdnZDxTLJzi6puFZExwA9FZD9VrcCCunSliaNJ06nFcRzHcVoD3mZwN0NEDhOR97HOHtXAkar6M+A2rMpXgTLg99gwMG+mGTblFKz93cmquhBAVf+KlRiOxao9k3JLw7kHAO+H/+2B9ap6MfBXYGr4PyGcCxHpA0zFbHFNCAQbygVYm8BBie13hzy4KKy3xTqbJHkpS2cVAaZnkf18uNbnY9s6AYjI8DCETp38chzHcZx8wYPB3Y/FwB+B0wFV1Xggcz21ewfvA7yb5hxPYb2O3xCR9SKyTkTKgcmqOgqrNk4yBOvNuww4PPzfEtv/AnCeiFwKPI719EVVVwIXYx1WGsvq0DZyaXyjqm7D2i12DJs6ETrLNCHxDiQRfYF1wBVhGdXEMh3HcRynyfBgcDdDVdeo6g1YUJikLPQCrlmAbRlOFQ2Hsr+qdsfa6GWTuylUxSoQ/Y/zJBYc/gq4RFXvjaV9nmZCVW9U1V+G1e7Amh09ZzSoNBb0xUsG+4b/o4CJqvp1Vf06du2O4ziOk5d4MOg0B0UiMjaxrRq4FRvgumcTy+sVOoDkmgpvIFBnnMNG8AqwBxYg/xy7np9jbQ/3wNpHvp4xteM4juPkER4M7maISIGIfBab9SPZc/bXyWnSsCrddCg2cPScMCTLRGC9iOwJ7J/m3HFexGYBAeggIndgJZWDsZlP7hKRCdHwNzmuZz8R+WRMz3RyH8U6gCxKk75bGIh7NFaVOy15DDA221R0JPJIVSux0r+/Al1UtQzrdPNb4GigP/A3sbmaD8N6bGcaBsdxHMdxWhTvTbybEXoLX4sNJ/N0Yve1wCOJbU+RhjBeYffkdhGZjgU3t2dQ4TYsMHsCG8OwDOiNzRP8pKpWisg84HJgYz0u6UDgl9jg2P9S1XWJ/QuBq1V1Spgm7jJqD0vzbeAHWInkBNJX2TZmOrrvAX/ABrIGmwN5FjZN3SOqWioiB2GBcQFwYY7rdBzHcZwWQRrXedNxWjciUhgCZsdxHMfZpfFg0HEcx3EcpxXjbQYdx3Ecx3FaMR4MOo7jOI7jtGI8GHQcx3Ecx2nFeDDoOI7jOI7TivFg0HEcx3EcpxXjwaDjOI7jOE4rxoNBx3Ecx3GcVsz/A5gCeHUnwVHDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  \n",
    "df = get_price('600036.XSHG',start_date='2022-03-1', end_date='2022-6-12', frequency='daily')\n",
    "close = df['close']   #收盘价\n",
    "volume = df['volume']   #成交量\n",
    "matplotlib.rcParams.update({'font.size': 15, 'font.family': 'serif'})\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10,3))\n",
    "axes[0].plot(close, label=\"招商银行的收盘价\")\n",
    "axes[0].plot(volume, label=\"招商银行的成交量\")\n",
    "axes[0].legend(loc=2); # 左上角\n",
    "axes[0].set_xlabel('招商银行的日期')\n",
    "axes[0].set_ylabel('招商银行的收盘价和成交量')\n",
    "axes[0].set_title('招商银行的日线图')\n",
    "axes[0].grid(True)\n",
    "\n",
    "money = df['money']   # 成交量\n",
    "axes[1].plot(money, label=\"招商银行的成交量\")\n",
    "axes[1].legend(loc=0)\n",
    "axes[0].set_xlabel('招商银行的日期')\n",
    "axes[1].set_ylabel('招商银行的成交量')\n",
    "axes[1].set_title('招商银行的日线图')\n",
    "axes[1].grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考原文链接：https://blog.csdn.net/Corollary/article/details/105391017\n",
    "\n",
    "格式化字符\n",
    "格式\t样式\n",
    "'-'\t实线样式\n",
    "'–'\t短横线样式\n",
    "'-.'\t点划线样式\n",
    "':'\t虚线样式\n",
    "'.'\t点标记\n",
    "','\t像素标记\n",
    "'o'\t圆标记\n",
    "'v'\t倒三角标记\n",
    "'^'\t正三角标记\n",
    "'<'\t左三角标记\n",
    "'>'\t右三角标记\n",
    "'1'\t下箭头标记\n",
    "'2'\t上箭头标记\n",
    "'3'\t左箭头标记\n",
    "'4'\t右箭头标记\n",
    "'s'\t正方形标记\n",
    "'p'\t五边形标记\n",
    "'*'\t星形标记\n",
    "“h”\t六边形标记 1\n",
    "“H”\t六边形标记 2\n",
    "'+'\t加号标记\n",
    "“x”\tX 标记\n",
    "“D”\t菱形标记\n",
    "'d'\t窄菱形标记\n",
    "'|'\t竖直线标记\n",
    "'_'\t水平线标记\n",
    "\n",
    "常用色彩\n",
    "代码\t色彩\n",
    "'b'\t蓝色\n",
    "'g'\t绿色\n",
    "'r'\t红色\n",
    "'c'\t青色\n",
    "'m'\t品红色\n",
    "“和”\t黄色\n",
    "'k'\t黑色\n",
    "“在”\t白色"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考链接：https://www.jianshu.com/p/6b1dcb460f7a\n",
    "matplotlib中的legend主要用来设置图例相关的内容，其中loc用来表示图例的具体位置，他的可选的参数可以是字符，也可以是数字，默认情况下是0（即best），参数及意思如下：\n",
    "0: ‘best’ （自动寻找最好的位置）\n",
    "1: ‘upper right’ （右上角）\n",
    "2: ‘upper left’ （左上角）\n",
    "3: ‘lower left’ （左下角）\n",
    "4: ‘lower right’ （右下角）\n",
    "5: ‘right’ （右边中间）\n",
    "6: ‘center left’ （左边中间）\n",
    "7: ‘center right’ （右边中间）\n",
    "8: ‘lower center’ （中间最下面）\n",
    "9: ‘upper center’ （中间最上面）\n",
    "10: ‘center’ （正中心)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "MarkDown菜单",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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